1. Names and Honor Code¶

Derek Christensen¶

Honor code (“K-State Honor Pledge: "On my honor, as a student, I have neither given nor received unauthorized aid on this academic work.")¶

Import Needed libraries¶

# Import Needed libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

from patsy import dmatrices

#import decisiontreeclassifier
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
#import randomforest classifier
from sklearn.ensemble import RandomForestClassifier
#import logisticregression classifier
from sklearn.linear_model import LogisticRegression
import statsmodels.api as sm
#import knn classifier
from sklearn.neighbors import KNeighborsClassifier

#for validating your classification model
from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import cross_val_score
from sklearn import metrics
from sklearn.metrics import roc_curve, auc

# feature selection
from sklearn.feature_selection import RFE
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.grid_search import GridSearchCV

from IPython.display import Image
from IPython.core.display import HTML

pd.set_option('display.height', 1000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

height has been deprecated.

# input title slide here
Image('Images/christensen_finalprj/Slide1.png')

# input outline slide here
Image('Images/christensen_finalprj/Slide2.png')

# input problem description & introduction slide here
Image('Images/christensen_finalprj/Slide3.png')

2. Business Understanding & Data Understanding¶

Determine Business Objectives:¶

Background: Hospitals are penalized for patients that are re-admitted less than 30 days after they are released.

Business Objectives: To reduce or eliminate the number of patients re-admitted less than 30 days after they are released.

Success Criteria: Identification of factors that increse the likelihood of a patient returning within 30 days.

Business Value: The average cost in 2011 for a hospital stay was $10,000.*

*http://www.beckershospitalreview.com/finance/11-statistics-on-average-hospital-costs-per-stay.html

# embed full problem description web image
Image(url= "https://72022e22-a-62cb3a1a-s-sites.googlegroups.com/site/christensenfinalprj/full-problem-description/christensen_finalprj-full-prob-descrp.png?attachauth=ANoY7coojyQ211rGpA2ygjc39hleeQ1Z-4R5R-B88BLBFtH1jAq2nT7l4JsRJfQhXwLYsBJ-HR_9ZFkGPGhybCqgeeBS4QVGujnYQae0dHKQCi6coswid7_6nzzAJ-riEv5DfvoEe1sls3dzBnvabtMJMesqIkRfqQSISQBI-Bdpp1ZveQ__SDPGMfWoW4kK1rmIropOOyy_2QQXBqRlq1hCp7cN6UPYzTlp54LBVweAnkfNexPQsIZDh90sH3xMlJGsDWOoDZHAHlB16u69fTPBSXvO57EqRNRejBKcU2bYYujzMRFjMp0%3D&attredirects=0")

# input key findings & insights slide here
Image('Images/christensen_finalprj/Slide4.png')

# input key final analysis & recommendation slide here
Image('Images/christensen_finalprj/Slide5.png')

# input next steps slide here
Image('Images/christensen_finalprj/Slide6.png')

Next Steps Ideas¶

Analyse those close to the 30 day threshold - i.e. 31 to 45-60 days

Weight Data

Cross referencing between the 3 Diagnosis'

Analyzing the Order of the 3 Diagnosis'

Add more Diagnosis

More Granular in the Diagnosis

?

# input dataset slide here
Image('Images/christensen_finalprj/Slide7.png')

3. Data Identification & Collection¶

Determine Business Objectives:¶

Description: The dataset contains over 56,000 HIPPA compliant de-identified records of hospital admissions.

Source: Hack K-State 2016 : Data Science For Social Good - https://zslie.github.io/

Details: There are 50 columns, of which is the Visit ID and Patient ID, along with 48 factors.

Factors: The factors have varying number of attributes, ranging from 1 to 715, so there are ~5.27x10^41 solutions.

Factors: Descriptions below.

#embed factor descriptions 'fd'
fd = pd.read_excel('data/factor-definitions.xlsx')
fd

Performed some data manipulation directly in excel, including:¶

Changed 'medical_specialy' to 'MED_SPEC_NUM'

Changed the 3 <string/int> 'diag_x's to 'DIAG_CAT_X'S & converted 858 unique diagnosis' into 33 Diagnosis Categories

Notes are in Challenge_1_Training_Data_Conversion.xlsx file on the "Storage" page

#import patient data
df = pd.read_csv('data/Challenge_1_Training_Work_Clean.csv')
df.head(5)

# Number of unique values in each column
df.apply(pd.Series.nunique)

encounter_id                56000
patient_nbr                 44369
race                            6
gender                          2
age                            10
weight                         10
admission_type_id               8
discharge_disposition_id       26
admission_source_id            17
time_in_hospital               14
payer_code                     17
medical_specialty              64
MED_SPEC_NUM                   64
num_lab_procedures            114
num_procedures                  7
num_medications                73
number_outpatient              32
number_emergency               25
number_inpatient               21
diag_1                        661
DIAG_CAT_1                     31
diag_2                        668
DIAG_CAT_2                     30
diag_3                        715
DIAG_CAT_3                     30
number_diagnoses               16
max_glu_serum                   4
A1Cresult                       4
metformin                       4
repaglinide                     4
nateglinide                     4
chlorpropamide                  4
glimepiride                     4
acetohexamide                   2
glipizide                       4
glyburide                       4
tolbutamide                     2
pioglitazone                    4
rosiglitazone                   4
acarbose                        4
miglitol                        4
troglitazone                    2
tolazamide                      2
examide                         1
citoglipton                     1
insulin                         4
glyburide.metformin             4
glipizide.metformin             2
glimepiride.pioglitazone        1
metformin.rosiglitazone         2
metformin.pioglitazone          2
change                          2
diabetesMed                     2
readmitted                      3
dtype: int64

#show the information about the data'
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 54 columns):
encounter_id                56000 non-null int64
patient_nbr                 56000 non-null int64
race                        56000 non-null object
gender                      56000 non-null object
age                         56000 non-null object
weight                      56000 non-null object
admission_type_id           56000 non-null int64
discharge_disposition_id    56000 non-null int64
admission_source_id         56000 non-null int64
time_in_hospital            56000 non-null int64
payer_code                  56000 non-null object
medical_specialty           56000 non-null object
MED_SPEC_NUM                56000 non-null int64
num_lab_procedures          56000 non-null int64
num_procedures              56000 non-null int64
num_medications             56000 non-null int64
number_outpatient           56000 non-null int64
number_emergency            56000 non-null int64
number_inpatient            56000 non-null int64
diag_1                      56000 non-null object
DIAG_CAT_1                  56000 non-null int64
diag_2                      56000 non-null object
DIAG_CAT_2                  56000 non-null int64
diag_3                      56000 non-null object
DIAG_CAT_3                  56000 non-null int64
number_diagnoses            56000 non-null int64
max_glu_serum               56000 non-null object
A1Cresult                   56000 non-null object
metformin                   56000 non-null object
repaglinide                 56000 non-null object
nateglinide                 56000 non-null object
chlorpropamide              56000 non-null object
glimepiride                 56000 non-null object
acetohexamide               56000 non-null object
glipizide                   56000 non-null object
glyburide                   56000 non-null object
tolbutamide                 56000 non-null object
pioglitazone                56000 non-null object
rosiglitazone               56000 non-null object
acarbose                    56000 non-null object
miglitol                    56000 non-null object
troglitazone                56000 non-null object
tolazamide                  56000 non-null object
examide                     56000 non-null object
citoglipton                 56000 non-null object
insulin                     56000 non-null object
glyburide.metformin         56000 non-null object
glipizide.metformin         56000 non-null object
glimepiride.pioglitazone    56000 non-null object
metformin.rosiglitazone     56000 non-null object
metformin.pioglitazone      56000 non-null object
change                      56000 non-null object
diabetesMed                 56000 non-null object
readmitted                  56000 non-null object
dtypes: int64(17), object(37)
memory usage: 23.1+ MB

#describe the column readmitted only (e.g., count, unique, frequency)
df['readmitted'].describe()

count     56000
unique        3
top          NO
freq      30238
Name: readmitted, dtype: object

#distribution of 0 and 1 in the readmitted column
df.groupby('readmitted').count()

#replace the values of the 'readmitted' column:
# NO = 0
# >30 = 1
# <30 = 2

df = df.replace({'readmitted': {'NO': 0, '>30': 1, '<30': 2}})

df.head(2)

Key Business Data Question Summary¶

Of 56,000 hospital visits in this dB:

6,285 were re-admitted < 30 days - these are the instances that need solved for

19,477 were also re-admitted, but after the 30 day threshold

30,238 were not re-admitted - there could be some insight also gleaned from why they DID'T have to be re-admitted

# input ETL slide here
Image('Images/christensen_finalprj/Slide8.png')

Data understanding & processing (ETL)¶

#drop or remove the columns 'encounter_id', 'patient_nbr' since this column is not used in the analysis and disply the result
df = df.drop('encounter_id', axis=1)
df = df.drop('patient_nbr', axis=1)
df = df.drop('medical_specialty', axis=1)

# drop or remove the columns 'diag_1', 'diag_2' and 'diag_3' since these values of been put into catergories
# in columns 'DIAG_CAT_1', 'DIAG_CAT_2' and 'DIAG_CAT_3'
df = df.drop('diag_1', axis=1)
df = df.drop('diag_2', axis=1)
df = df.drop('diag_3', axis=1)

df.head(5)

#distribution of races in the race column
df.groupby('race').count()

#replace the values of the 'race' column:
# ? = 0
# AfricanAmerican = 1
# Asian = 2
# Caucasion = 3
# Hispanic = 4
# Other = 5

df = df.replace({'race': {'?': 0, 'AfricanAmerican': 1, 'Asian': 2,'Caucasian': 3,'Hispanic': 4,'Other': 5}})

df.head(2)

#distribution of genders in the gender column
df.groupby('gender').count()

#replace the values of the 'gender' column:
# Female = 0
# Male = 1

df = df.replace({'gender': {'Male': 1, 'Female': 0}})

df.head(2)

#distribution of decade age categories in the age column
df.groupby('age').count()

#replace the values of the 'age' column:
# [0-10) = 0
# [10-20) = 1
# [20-30) = 2
# [30-40) = 3
# [40-50) = 4
# [50-60) = 5
# [60-70) = 6
# [70-80) = 7
# [80-90) = 8
# [90-100) = 9

df = df.replace({'age': {'[0-10)': 0, '[10-20)': 1, '[20-30)': 2, '[30-40)': 3, '[40-50)': 4, '[50-60)': 5, '[60-70)': 6, '[70-80)': 7, '[80-90)': 8, '[90-100)': 9}})

df.head(2)

#distribution of weight categories in the weight column
df.groupby('weight').count()

#replace the values of the 'weight' column:
# ? = 0
# [0-25) = 1
# [25-50) = 2
# [50-75) = 3
# [75-100) = 4
# [100-125) = 5
# [125-150) = 6
# [150-175) = 7
# [175-200) = 8
# > 200 = 9

df = df.replace({'weight': {'?': 0, '[0-25)': 1, '[25-50)': 2, '[50-75)': 3, '[75-100)': 4, '[100-125)': 5, '[125-150)': 6, '[150-175)': 7, '[175-200)': 8, '>200': 9}})

df.head(2)

#distribution of pay types in the payer_code column
df.groupby('payer_code').count()

#replace the values of the 'payer_code' column:

df = df.replace({'payer_code': {'?': 0, 'BC': 1, 'CH': 2, 'CM': 3, 'CP': 4, 'DM': 5, 'HM': 6, 'MC': 7, 'MD': 8, 'MP': 9, 'OG': 10, 'OT': 11, 'PO': 12, 'SI': 13, 'SP': 14, 'UN': 15, 'WC': 16}})

df.head(2)

#distribution of categories in the max_glu_serum column
df.groupby('max_glu_serum').count()

#replace the values of the 'max_glu_serum' column:
# None = 0
# Norm = 1
# >200 = 2
# >300 = 3

df = df.replace({'max_glu_serum': {'None': 0, 'Norm': 1, '>200': 2, '>300': 3}})

df.head(2)

#distribution of categories in the A1Cresult column
df.groupby('A1Cresult').count()

#replace the values of the 'A1Cresult' column:
# None = 0
# Norm = 1
# >7 = 2
# >8 = 3

df = df.replace({'A1Cresult': {'None': 0, 'Norm': 1, '>7': 2, '>8': 3}})

df.head(2)

#distribution of Ch or No in the change column
df.groupby('change').count()

#replace the values of the 'change' column:
# No = 0
# Ch = 1

df = df.replace({'change': {'No': 0, 'Ch': 1}})

df.head(2)

#distribution of No or Yes in the diabetesMed column
df.groupby('diabetesMed').count()

#replace the values of the 'diabetesMed' column:
# No = 0
# Yes = 1

df = df.replace({'diabetesMed': {'No': 0, 'Yes': 1,}})

df.head(2)

#distribution of the medical specialty categories in the MED_SPEC_NUM column
df.groupby('MED_SPEC_NUM').count()

#distribution of diagnosis categories in the DIAG_CAT_1 column
df.groupby('DIAG_CAT_1').count()

#distribution of diagnosis categories in the DIAG_CAT_2 column
df.groupby('DIAG_CAT_2').count()

#distribution of diagnosis categories in the DIAG_CAT_3 column
df.groupby('DIAG_CAT_3').count()

#replace the values in the medicene column:
# No = 0
# Down = 1
# Steady = 2
# Up = 3

df = df.replace({'metformin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'repaglinide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'nateglinide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'chlorpropamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'glimepiride': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'acetohexamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'glipizide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'glyburide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'tolbutamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'pioglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'rosiglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'acarbose': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'miglitol': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'troglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'tolazamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'examide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'citoglipton': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'insulin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'glyburide.metformin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'glipizide.metformin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'glimepiride.pioglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'metformin.rosiglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
df = df.replace({'metformin.pioglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})

df.head(2)

# check to make sure all factors are now int
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 48 columns):
race                        56000 non-null int64
gender                      56000 non-null int64
age                         56000 non-null int64
weight                      56000 non-null int64
admission_type_id           56000 non-null int64
discharge_disposition_id    56000 non-null int64
admission_source_id         56000 non-null int64
time_in_hospital            56000 non-null int64
payer_code                  56000 non-null int64
MED_SPEC_NUM                56000 non-null int64
num_lab_procedures          56000 non-null int64
num_procedures              56000 non-null int64
num_medications             56000 non-null int64
number_outpatient           56000 non-null int64
number_emergency            56000 non-null int64
number_inpatient            56000 non-null int64
DIAG_CAT_1                  56000 non-null int64
DIAG_CAT_2                  56000 non-null int64
DIAG_CAT_3                  56000 non-null int64
number_diagnoses            56000 non-null int64
max_glu_serum               56000 non-null int64
A1Cresult                   56000 non-null int64
metformin                   56000 non-null int64
repaglinide                 56000 non-null int64
nateglinide                 56000 non-null int64
chlorpropamide              56000 non-null int64
glimepiride                 56000 non-null int64
acetohexamide               56000 non-null int64
glipizide                   56000 non-null int64
glyburide                   56000 non-null int64
tolbutamide                 56000 non-null int64
pioglitazone                56000 non-null int64
rosiglitazone               56000 non-null int64
acarbose                    56000 non-null int64
miglitol                    56000 non-null int64
troglitazone                56000 non-null int64
tolazamide                  56000 non-null int64
examide                     56000 non-null int64
citoglipton                 56000 non-null int64
insulin                     56000 non-null int64
glyburide.metformin         56000 non-null int64
glipizide.metformin         56000 non-null int64
glimepiride.pioglitazone    56000 non-null int64
metformin.rosiglitazone     56000 non-null int64
metformin.pioglitazone      56000 non-null int64
change                      56000 non-null int64
diabetesMed                 56000 non-null int64
readmitted                  56000 non-null int64
dtypes: int64(48)
memory usage: 20.5 MB

# save converted data frame with only int to a new file
df_clean_NoString = df

# check to make sure all factors of the new data frame are int
df_clean_NoString.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 48 columns):
race                        56000 non-null int64
gender                      56000 non-null int64
age                         56000 non-null int64
weight                      56000 non-null int64
admission_type_id           56000 non-null int64
discharge_disposition_id    56000 non-null int64
admission_source_id         56000 non-null int64
time_in_hospital            56000 non-null int64
payer_code                  56000 non-null int64
MED_SPEC_NUM                56000 non-null int64
num_lab_procedures          56000 non-null int64
num_procedures              56000 non-null int64
num_medications             56000 non-null int64
number_outpatient           56000 non-null int64
number_emergency            56000 non-null int64
number_inpatient            56000 non-null int64
DIAG_CAT_1                  56000 non-null int64
DIAG_CAT_2                  56000 non-null int64
DIAG_CAT_3                  56000 non-null int64
number_diagnoses            56000 non-null int64
max_glu_serum               56000 non-null int64
A1Cresult                   56000 non-null int64
metformin                   56000 non-null int64
repaglinide                 56000 non-null int64
nateglinide                 56000 non-null int64
chlorpropamide              56000 non-null int64
glimepiride                 56000 non-null int64
acetohexamide               56000 non-null int64
glipizide                   56000 non-null int64
glyburide                   56000 non-null int64
tolbutamide                 56000 non-null int64
pioglitazone                56000 non-null int64
rosiglitazone               56000 non-null int64
acarbose                    56000 non-null int64
miglitol                    56000 non-null int64
troglitazone                56000 non-null int64
tolazamide                  56000 non-null int64
examide                     56000 non-null int64
citoglipton                 56000 non-null int64
insulin                     56000 non-null int64
glyburide.metformin         56000 non-null int64
glipizide.metformin         56000 non-null int64
glimepiride.pioglitazone    56000 non-null int64
metformin.rosiglitazone     56000 non-null int64
metformin.pioglitazone      56000 non-null int64
change                      56000 non-null int64
diabetesMed                 56000 non-null int64
readmitted                  56000 non-null int64
dtypes: int64(48)
memory usage: 20.5 MB

# write dataframe with no string values to new csv file
df_clean_NoString.to_csv('data/Challenge_1_Training_Work_Clean_NoString.csv')

# input exploratory analysis slide here
Image('Images/christensen_finalprj/Slide9.png')

Exploratory data analysis¶

#import ETL patient data
df = pd.read_csv('data/Challenge_1_Training_Work_Clean_NoString.csv')
df.head(5)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 49 columns):
Unnamed: 0                  56000 non-null int64
race                        56000 non-null int64
gender                      56000 non-null int64
age                         56000 non-null int64
weight                      56000 non-null int64
admission_type_id           56000 non-null int64
discharge_disposition_id    56000 non-null int64
admission_source_id         56000 non-null int64
time_in_hospital            56000 non-null int64
payer_code                  56000 non-null int64
MED_SPEC_NUM                56000 non-null int64
num_lab_procedures          56000 non-null int64
num_procedures              56000 non-null int64
num_medications             56000 non-null int64
number_outpatient           56000 non-null int64
number_emergency            56000 non-null int64
number_inpatient            56000 non-null int64
DIAG_CAT_1                  56000 non-null int64
DIAG_CAT_2                  56000 non-null int64
DIAG_CAT_3                  56000 non-null int64
number_diagnoses            56000 non-null int64
max_glu_serum               56000 non-null int64
A1Cresult                   56000 non-null int64
metformin                   56000 non-null int64
repaglinide                 56000 non-null int64
nateglinide                 56000 non-null int64
chlorpropamide              56000 non-null int64
glimepiride                 56000 non-null int64
acetohexamide               56000 non-null int64
glipizide                   56000 non-null int64
glyburide                   56000 non-null int64
tolbutamide                 56000 non-null int64
pioglitazone                56000 non-null int64
rosiglitazone               56000 non-null int64
acarbose                    56000 non-null int64
miglitol                    56000 non-null int64
troglitazone                56000 non-null int64
tolazamide                  56000 non-null int64
examide                     56000 non-null int64
citoglipton                 56000 non-null int64
insulin                     56000 non-null int64
glyburide.metformin         56000 non-null int64
glipizide.metformin         56000 non-null int64
glimepiride.pioglitazone    56000 non-null int64
metformin.rosiglitazone     56000 non-null int64
metformin.pioglitazone      56000 non-null int64
change                      56000 non-null int64
diabetesMed                 56000 non-null int64
readmitted                  56000 non-null int64
dtypes: int64(49)
memory usage: 20.9 MB

#drop or remove the column 'Unnamed: 0' since this column is not used in the analysis and disply the result
df = df.drop('Unnamed: 0', axis=1)
df.head(2)

# basic statistics
df.describe()

Basic Statistics Notes¶

mean: caucasion, female, 60's, Urgent, Discharged/Txfr'd, Txfr from facility, 4.4 days, CP payer, 43 lab procedures, 16 meds,

0.4 out patient visit prev yr, 0.2 ER visits, 0.64 Inpatient, 7.4 diag's, 0.09 Gluc, Ai 0.4,

Several Meds at 0 to little use - need to eliminate some meds

Need to use dummy variables for 'readmitted' and combine No/0 and >30/1, since the question is if admitted <30 only

# correlation analysis
df.corr()

Correlation Analysis Notes:¶

Lots of noise - need to narrow

Strongest +'s:

#ER = 0.103
#Inpatient = 0.233
#Diag's = 0.104

Strongest -'s:

MED_SPEC_NUM: -0.045 --> Which is irrelevant at this point as they are alphabetically sorted
#Procedure's: -0.038
metformin: -0.036

No Correlation w/ several Med's

#drop or remove these columns since they are not used in any of the cases
df = df.drop('examide', axis=1)
df = df.drop('citoglipton', axis=1)
df = df.drop('glimepiride.pioglitazone', axis=1)

df.head(5)

#basic statistics
df.describe()

# correlation analysis
df.corr()

Preliminary possibilites correlated with readmitted¶

number_emergency = 0.103321

number_inpatient = 0.233149

number_diagnoses = 0.103885

No change from previous correlation analysis Strongest +'s:

#ER = 0.103

#Inpatient = 0.233

#Diag's = 0.104 Strongest -'s:

MED_SPEC_NUM: -0.045 --> Which is irrelevant at this point as they are alphabetically sorted

#Porcedure's: -0.038

metformin: -0.036

# heatmap for correlation
plt.figure(figsize=(35,36))
sns.heatmap(df.corr(), annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x37dce6d8>

# describe for a single column
df['readmitted'].describe()

count    56000.000000
mean         0.572268
std          0.685018
min          0.000000
25%          0.000000
50%          0.000000
75%          1.000000
max          2.000000
Name: readmitted, dtype: float64

# how many unique values in the 'readmitted' column
df.groupby('readmitted').size()

readmitted
0    30238
1    19477
2     6285
dtype: int64

# how many missing values in each column or variable
df.isnull().sum()

race                        0
gender                      0
age                         0
weight                      0
admission_type_id           0
discharge_disposition_id    0
admission_source_id         0
time_in_hospital            0
payer_code                  0
MED_SPEC_NUM                0
num_lab_procedures          0
num_procedures              0
num_medications             0
number_outpatient           0
number_emergency            0
number_inpatient            0
DIAG_CAT_1                  0
DIAG_CAT_2                  0
DIAG_CAT_3                  0
number_diagnoses            0
max_glu_serum               0
A1Cresult                   0
metformin                   0
repaglinide                 0
nateglinide                 0
chlorpropamide              0
glimepiride                 0
acetohexamide               0
glipizide                   0
glyburide                   0
tolbutamide                 0
pioglitazone                0
rosiglitazone               0
acarbose                    0
miglitol                    0
troglitazone                0
tolazamide                  0
insulin                     0
glyburide.metformin         0
glipizide.metformin         0
metformin.rosiglitazone     0
metformin.pioglitazone      0
change                      0
diabetesMed                 0
readmitted                  0
dtype: int64

# pivot table for 'readmitted'
df.groupby(['readmitted']).count()

# pivot table for 'readmitted' showing mean value, not count
df.groupby(['readmitted']).mean()

Mean 'readmitted' Pivot Table Notes:¶

Doesn't appear to be a factor:

race

gender

admission_type_id / admission

payer_code

Slight possiblity of being a factor:

age

num_lab_procedures

num_procedures (interesting that those not returning at all had the highest average number of procedures at 1.404)

num_medications

number_diagnoses

insulin

change

diabetesMed

Appears to be a possible factor:

weight

discharge_disposition_id

time_in_hospital

number_outpatient

number_emergency

number_inpatient

max_gluc_serum

A1Cresult (negative factor)

metformin (neg)

#histograms for all factors
df.hist(figsize=(16,16))

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Since the goal is to find out what causes a 'readmitted' < 30 days,¶
need to combine those not readmitted and those readmitted > 30 days.¶

#create 2nd 'readmitted' column
df['readm2'] = df['readmitted']
df.head(12)

NB: Using 12 rows because the 12th row (row 11) is the first instance of a patient being readmitted < 30 days¶

#replace the values of the 'readm2' column:
# NO = 0  ==> 0 -> NO
# >30 = 1 ==> 1 -> >30
# <30 = 2 ==> 2 -> <30

df = df.replace({'readm2': {0: 'NO', 1: '>30', 2: '<30'}})

df.head(12)

#replace the values of the 'readm2' column:
# NO = 0  ==> 0 -> NO ==> NO -> 0
# >30 = 1 ==> 1 -> >30 ==> >30 -> 0
# <30 = 2 ==> 2 -> <30 ==> <30 -> 1

df = df.replace({'readm2': {'NO': 0, '>30': 0, '<30': 1}})

df.head(12)

# drop readmitted - will use readm2 as Y instead
df = df.drop('readmitted', axis=1)
df.head(12)

#basic statistics
df.describe()

# describe for a single column
df['readm2'].describe()

count    56000.000000
mean         0.112232
std          0.315655
min          0.000000
25%          0.000000
50%          0.000000
75%          0.000000
max          1.000000
Name: readm2, dtype: float64

# how many unique values in the 'readm2' column
df.groupby('readm2').size()

readm2
0    49715
1     6285
dtype: int64

# pivot talbe for 'readm2'
df.groupby(['readm2']).count()

# pivot table for 'pep' showing mean value, not count
df.groupby(['readm2']).mean()

#regression correlation
df.corr()

Preliminary possibilites correlated with readm2 has changed versus readmitted¶

number_emergency = 0.103321 ==> No longer is showing significant correlation now at 0.053

number_inpatient = 0.233149 ==> Is now the only one showing any significant correlation at 0.162

number_diagnoses = 0.103885 ==> No longer is showing significant correlation now at 0.045

# heatmap for correlation
plt.figure(figsize=(35,36))
sns.heatmap(df.corr(), annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x484f2c88>

# input model building slide here
Image('Images/christensen_finalprj/Slide10.png')

Classification Model building.¶

Need to narrow down the number of factors in order to focus on the most significant ones

ExtraTreeClassifier¶

# Set Y and X

y = df['readm2']
X = df.drop(['readm2'], axis=1)
X.head(12)

# build logisticRegression

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

lr = LogisticRegression()
lr.fit(X_train, y_train)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

# build ExtraTreesClassifier

model_extra = ExtraTreesClassifier()
model_extra.fit(X, y)
model_extra.score(X, y)

# display the relative importance of each attribute
print(model_extra.feature_importances_)

[  2.91794982e-02   2.53679647e-02   5.37078564e-02   7.50331122e-03
   3.35746117e-02   4.57729444e-02   2.97595433e-02   5.66506897e-02
   4.27513308e-02   4.18174543e-02   6.59548839e-02   4.37842852e-02
   6.46074445e-02   2.93875786e-02   2.45889522e-02   5.46483089e-02
   5.79017039e-02   5.91816193e-02   5.91892575e-02   4.36703730e-02
   7.38929730e-03   1.52840833e-02   1.17254508e-02   4.00583871e-03
   2.24838652e-03   8.73138518e-05   8.64359359e-03   1.86232908e-06
   1.34819531e-02   1.19863156e-02   2.40050594e-05   9.38387138e-03
   7.51014406e-03   1.08885851e-03   1.67479741e-04   4.64248888e-06
   3.14279024e-05   2.30591406e-02   1.41216087e-03   8.83976179e-05
   0.00000000e+00   3.74683575e-07   9.02443469e-03   4.35135523e-03]

# What are the highest ranking X variables according to ExtraTreeClassifier?

print("Features sorted by their rank:")
print(sorted(zip(map(lambda x: round(x, 4), model_extra.feature_importances_), X.columns)))

Features sorted by their rank:
[(0.0, 'acetohexamide'), (0.0, 'metformin.pioglitazone'), (0.0, 'metformin.rosiglitazone'), (0.0, 'tolazamide'), (0.0, 'tolbutamide'), (0.0, 'troglitazone'), (0.0001, 'chlorpropamide'), (0.0001, 'glipizide.metformin'), (0.00020000000000000001, 'miglitol'), (0.0011000000000000001, 'acarbose'), (0.0014, 'glyburide.metformin'), (0.0022000000000000001, 'nateglinide'), (0.0040000000000000001, 'repaglinide'), (0.0044000000000000003, 'diabetesMed'), (0.0074000000000000003, 'max_glu_serum'), (0.0074999999999999997, 'rosiglitazone'), (0.0074999999999999997, 'weight'), (0.0086, 'glimepiride'), (0.0089999999999999993, 'change'), (0.0094000000000000004, 'pioglitazone'), (0.0117, 'metformin'), (0.012, 'glyburide'), (0.0135, 'glipizide'), (0.015299999999999999, 'A1Cresult'), (0.023099999999999999, 'insulin'), (0.0246, 'number_emergency'), (0.025399999999999999, 'gender'), (0.0292, 'race'), (0.029399999999999999, 'number_outpatient'), (0.0298, 'admission_source_id'), (0.033599999999999998, 'admission_type_id'), (0.041799999999999997, 'MED_SPEC_NUM'), (0.042799999999999998, 'payer_code'), (0.043700000000000003, 'number_diagnoses'), (0.043799999999999999, 'num_procedures'), (0.0458, 'discharge_disposition_id'), (0.053699999999999998, 'age'), (0.054600000000000003, 'number_inpatient'), (0.0567, 'time_in_hospital'), (0.0579, 'DIAG_CAT_1'), (0.059200000000000003, 'DIAG_CAT_2'), (0.059200000000000003, 'DIAG_CAT_3'), (0.064600000000000005, 'num_medications'), (0.066000000000000003, 'num_lab_procedures')]

All facotrs with less than 0.005 (rounded) will be removed¶

'repaglinide' at 0.0041 and less are dropped

'diabetesMed' at 0.0049 and above are kept

#drop or remove the column 'ID' since this column is not used in the analysis and disply the result
df = df.drop('acetohexamide', axis=1)
df = df.drop('metformin.pioglitazone', axis=1)
df = df.drop('metformin.rosiglitazone', axis=1)
df = df.drop('tolazamide', axis=1)

df = df.drop('tolbutamide', axis=1)
df = df.drop('troglitazone', axis=1)
df = df.drop('chlorpropamide', axis=1)
df = df.drop('glipizide.metformin', axis=1)
df = df.drop('miglitol', axis=1)
df = df.drop('acarbose', axis=1)
df = df.drop('glyburide.metformin', axis=1)
df = df.drop('nateglinide', axis=1)
df = df.drop('repaglinide', axis=1)


df.head(12)

#general info
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 32 columns):
race                        56000 non-null int64
gender                      56000 non-null int64
age                         56000 non-null int64
weight                      56000 non-null int64
admission_type_id           56000 non-null int64
discharge_disposition_id    56000 non-null int64
admission_source_id         56000 non-null int64
time_in_hospital            56000 non-null int64
payer_code                  56000 non-null int64
MED_SPEC_NUM                56000 non-null int64
num_lab_procedures          56000 non-null int64
num_procedures              56000 non-null int64
num_medications             56000 non-null int64
number_outpatient           56000 non-null int64
number_emergency            56000 non-null int64
number_inpatient            56000 non-null int64
DIAG_CAT_1                  56000 non-null int64
DIAG_CAT_2                  56000 non-null int64
DIAG_CAT_3                  56000 non-null int64
number_diagnoses            56000 non-null int64
max_glu_serum               56000 non-null int64
A1Cresult                   56000 non-null int64
metformin                   56000 non-null int64
glimepiride                 56000 non-null int64
glipizide                   56000 non-null int64
glyburide                   56000 non-null int64
pioglitazone                56000 non-null int64
rosiglitazone               56000 non-null int64
insulin                     56000 non-null int64
change                      56000 non-null int64
diabetesMed                 56000 non-null int64
readm2                      56000 non-null int64
dtypes: int64(32)
memory usage: 13.7 MB

#basic statistics
df.describe()

#correlation analysis
df.corr()

# heatmap for correlation
plt.figure(figsize=(35,36))
sns.heatmap(df.corr(), annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x4c30fc50>

Decision Tree Model Building, Validation, Evaluation¶

Remember the model should be "simple, but not too simple"

Going to Split Data into two Different Sets¶

Training Set

Test Set

# Set Y and X

y = df['readm2']
X = df.drop(['readm2'], axis=1)
X.head(12)

# evaluate the model by splitting into train (70%) and test sets (30%)
# http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

# name the model as "dt"

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

#Model evaluation
# http://scikit-learn.org/stable/modules/model_evaluation.html

print(metrics.accuracy_score(y_test, dt.predict(X_test)))
print(metrics.confusion_matrix(y_test, dt.predict(X_test)))
print(metrics.classification_report(y_test, dt.predict(X_test)))
print(metrics.roc_auc_score(y_test, dt.predict(X_test)))

# y-test is the acual y value in the testing dataset
# dt.predict(X_test) is the y value generated by your model
# If they are same, we can say your model is accurate.

0.796428571429
[[13017  1937]
 [ 1483   363]]
             precision    recall  f1-score   support

          0       0.90      0.87      0.88     14954
          1       0.16      0.20      0.18      1846

avg / total       0.82      0.80      0.81     16800

0.533555413199

Question: Interpret the results of confusion matrix¶
13032 correctly classified as those who will not be readmitted.

1922 misclassified as those who will be readmitted, but actually will not be readmitted

299 correctly classified as those who will be readmitted
1547 misclassified as those who will not be readmitted, but actually will be readmitted
- Model accuracy would therefore be calculated as:
   - (13012+383) / (13012+1942+1463+383) = 13395/16800 = 0.7973 ==> Expect to be 79.73% accurate when this model is applied to real-world situation.

Visualizing decision tree¶

There are two methods for this. You can use either method.

Using Graphviz software. For this option, you need to have GraphViz installed in your mahcine.

# Graphviz
tree.export_graphviz(dt, out_file='data/decisiontree.dot', feature_names=X.columns)

# This is a "full-grown" tree 
from IPython.display import Image
Image("data/decisiontree.png")

Interpreting decision tree¶

Practical Sized Decision Tree

# Set Y and X

y = df['readm2']
X = df.drop(['readm2'], axis=1)

#max_depth = 5 ... otherwise you will get a full-grown tree, which is overfitting

# You can make a simpler decision tree

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
dt_simple = DecisionTreeClassifier(max_depth=3, min_samples_leaf=5)
dt_simple.fit(X_train, y_train)

# max_depth : The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

# min_samples_leaf : The minimum number of samples required to be at a leaf node

# http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=5,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

# Find out the performance of this model & interpret the results

print(metrics.accuracy_score(y_test, dt_simple.predict(X_test)))
print(metrics.confusion_matrix(y_test, dt_simple.predict(X_test)))
print(metrics.classification_report(y_test, dt_simple.predict(X_test)))
print(metrics.roc_auc_score(y_test, dt_simple.predict(X_test)))

0.889761904762
[[14946     8]
 [ 1844     2]]
             precision    recall  f1-score   support

          0       0.89      1.00      0.94     14954
          1       0.20      0.00      0.00      1846

avg / total       0.81      0.89      0.84     16800

0.500274224849

# Visualize the simpler decision tree model (dt_simple)

tree.export_graphviz(dt_simple, out_file='data/decisiontree_simple.dot', feature_names=X.columns)

# Embed decision tree

from IPython.display import Image
Image("data/decisiontree_simple.png")

Model Deployment: Predict y values¶

load Challenge_1_Validation_Work.csv (scoring dataset).

This dataset has no y value, represeting the future.

Apply your decision model and find out who is likely to be readmitted.

#import scoring data

#no Y value in this dataset ... 
#we are trying to predict whether the people in this scoring dataset are likely to be readmitted <30 days or not

score = pd.read_csv('data/Challenge_1_Validation_Work.csv')
score.head(5)

#drop or remove the columns 'encounter_id', 'patient_nbr' since this column is not used in the analysis and disply the result
score = score.drop('encounter_id', axis=1)
score = score.drop('patient_nbr', axis=1)
score = score.drop('medical_specialty', axis=1)

# drop or remove the columns 'diag_1', 'diag_2' and 'diag_3' since these values of been put into catergories
# in columns 'DIAG_CAT_1', 'DIAG_CAT_2' and 'DIAG_CAT_3'
score = score.drop('diag_1', axis=1)
score = score.drop('diag_2', axis=1)
score = score.drop('diag_3', axis=1)

score.head(5)

#replace the values of the 'race' column:
# ? = 0
# AfricanAmerican = 1
# Asian = 2
# Caucasion = 3
# Hispanic = 4
# Other = 5

score = score.replace({'race': {'?': 0, 'AfricanAmerican': 1, 'Asian': 2,'Caucasian': 3,'Hispanic': 4,'Other': 5}})

score.head(2)

#replace the values of the 'gender' column:
# Female = 0
# Male = 1

score = score.replace({'gender': {'Male': 1, 'Female': 0}})

score.head(2)

#replace the values of the 'age' column:
# [0-10) = 0
# [10-20) = 1
# [20-30) = 2
# [30-40) = 3
# [40-50) = 4
# [50-60) = 5
# [60-70) = 6
# [70-80) = 7
# [80-90) = 8
# [90-100) = 9

score = score.replace({'age': {'[0-10)': 0, '[10-20)': 1, '[20-30)': 2, '[30-40)': 3, '[40-50)': 4, '[50-60)': 5, '[60-70)': 6, '[70-80)': 7, '[80-90)': 8, '[90-100)': 9}})

score.head(2)

#replace the values of the 'weight' column:
# ? = 0
# [0-25) = 1
# [25-50) = 2
# [50-75) = 3
# [75-100) = 4
# [100-125) = 5
# [125-150) = 6
# [150-175) = 7
# [175-200) = 8
# > 200 = 9

score = score.replace({'weight': {'?': 0, '[0-25)': 1, '[25-50)': 2, '[50-75)': 3, '[75-100)': 4, '[100-125)': 5, '[125-150)': 6, '[150-175)': 7, '[175-200)': 8, '>200': 9}})

score.head(2)

#replace the values of the 'payer_code' column:

score = score.replace({'payer_code': {'?': 0, 'BC': 1, 'CH': 2, 'CM': 3, 'CP': 4, 'DM': 5, 'HM': 6, 'MC': 7, 'MD': 8, 'MP': 9, 'OG': 10, 'OT': 11, 'PO': 12, 'SI': 13, 'SP': 14, 'UN': 15, 'WC': 16, 'FR': 19}})

score.head(2)

#distribution of payer_code categories in the payer_code column
score.groupby('payer_code').count()

score.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14000 entries, 0 to 13999
Data columns (total 47 columns):
race                        14000 non-null int64
gender                      14000 non-null int64
age                         14000 non-null int64
weight                      14000 non-null int64
admission_type_id           14000 non-null int64
discharge_disposition_id    14000 non-null int64
admission_source_id         14000 non-null int64
time_in_hospital            14000 non-null int64
payer_code                  14000 non-null int64
MED_SPEC_NUM                14000 non-null int64
num_lab_procedures          14000 non-null int64
num_procedures              14000 non-null int64
num_medications             14000 non-null int64
number_outpatient           14000 non-null int64
number_emergency            14000 non-null int64
number_inpatient            14000 non-null int64
DIAG_CAT_1                  14000 non-null int64
DIAG_CAT_2                  14000 non-null int64
DIAG_CAT_3                  14000 non-null int64
number_diagnoses            14000 non-null int64
max_glu_serum               14000 non-null object
A1Cresult                   14000 non-null object
metformin                   14000 non-null object
repaglinide                 14000 non-null object
nateglinide                 14000 non-null object
chlorpropamide              14000 non-null object
glimepiride                 14000 non-null object
acetohexamide               14000 non-null object
glipizide                   14000 non-null object
glyburide                   14000 non-null object
tolbutamide                 14000 non-null object
pioglitazone                14000 non-null object
rosiglitazone               14000 non-null object
acarbose                    14000 non-null object
miglitol                    14000 non-null object
troglitazone                14000 non-null object
tolazamide                  14000 non-null object
examide                     14000 non-null object
citoglipton                 14000 non-null object
insulin                     14000 non-null object
glyburide.metformin         14000 non-null object
glipizide.metformin         14000 non-null object
glimepiride.pioglitazone    14000 non-null object
metformin.rosiglitazone     14000 non-null object
metformin.pioglitazone      14000 non-null object
change                      14000 non-null object
diabetesMed                 14000 non-null object
dtypes: int64(20), object(27)
memory usage: 5.0+ MB

#replace the values of the 'max_glu_serum' column:
# None = 0
# Norm = 1
# >200 = 2
# >300 = 3

score = score.replace({'max_glu_serum': {'None': 0, 'Norm': 1, '>200': 2, '>300': 3}})

score.head(2)

#replace the values of the 'A1Cresult' column:
# None = 0
# Norm = 1
# >7 = 2
# >8 = 3

score = score.replace({'A1Cresult': {'None': 0, 'Norm': 1, '>7': 2, '>8': 3}})

score.head(2)

#replace the values of the 'change' column:
# No = 0
# Ch = 1

score = score.replace({'change': {'No': 0, 'Ch': 1}})

score.head(2)

#replace the values of the 'diabetesMed' column:
# No = 0
# Yes = 1

score = score.replace({'diabetesMed': {'No': 0, 'Yes': 1,}})

score.head(2)

#replace the values in the medicene column:
# No = 0
# Down = 1
# Steady = 2
# Up = 3

score = score.replace({'metformin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'repaglinide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'nateglinide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'chlorpropamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'glimepiride': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'acetohexamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'glipizide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'glyburide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'tolbutamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'pioglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'rosiglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'acarbose': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'miglitol': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'troglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'tolazamide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'examide': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'citoglipton': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'insulin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'glyburide.metformin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'glipizide.metformin': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'glimepiride.pioglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'metformin.rosiglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})
score = score.replace({'metformin.pioglitazone': {'No': 0, 'Down': 1, 'Steady': 2, 'Up': 3}})

score.head(2)

# save converted data frame with only int to a new file
score_clean_NoString = score

#info
score_clean_NoString.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14000 entries, 0 to 13999
Data columns (total 47 columns):
race                        14000 non-null int64
gender                      14000 non-null int64
age                         14000 non-null int64
weight                      14000 non-null int64
admission_type_id           14000 non-null int64
discharge_disposition_id    14000 non-null int64
admission_source_id         14000 non-null int64
time_in_hospital            14000 non-null int64
payer_code                  14000 non-null int64
MED_SPEC_NUM                14000 non-null int64
num_lab_procedures          14000 non-null int64
num_procedures              14000 non-null int64
num_medications             14000 non-null int64
number_outpatient           14000 non-null int64
number_emergency            14000 non-null int64
number_inpatient            14000 non-null int64
DIAG_CAT_1                  14000 non-null int64
DIAG_CAT_2                  14000 non-null int64
DIAG_CAT_3                  14000 non-null int64
number_diagnoses            14000 non-null int64
max_glu_serum               14000 non-null int64
A1Cresult                   14000 non-null int64
metformin                   14000 non-null int64
repaglinide                 14000 non-null int64
nateglinide                 14000 non-null int64
chlorpropamide              14000 non-null int64
glimepiride                 14000 non-null int64
acetohexamide               14000 non-null int64
glipizide                   14000 non-null int64
glyburide                   14000 non-null int64
tolbutamide                 14000 non-null int64
pioglitazone                14000 non-null int64
rosiglitazone               14000 non-null int64
acarbose                    14000 non-null int64
miglitol                    14000 non-null int64
troglitazone                14000 non-null int64
tolazamide                  14000 non-null int64
examide                     14000 non-null int64
citoglipton                 14000 non-null int64
insulin                     14000 non-null int64
glyburide.metformin         14000 non-null int64
glipizide.metformin         14000 non-null int64
glimepiride.pioglitazone    14000 non-null int64
metformin.rosiglitazone     14000 non-null int64
metformin.pioglitazone      14000 non-null int64
change                      14000 non-null int64
diabetesMed                 14000 non-null int64
dtypes: int64(47)
memory usage: 5.0 MB

# write dataframe with no string values to new csv file
score_clean_NoString.to_csv('data/Challenge_1_Validation_Work_Clean_NoString.csv')

#score = pd.read_csv('data/Challenge_1_Validation_Work.csv')

Random Forest¶

score.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14000 entries, 0 to 13999
Data columns (total 47 columns):
race                        14000 non-null int64
gender                      14000 non-null int64
age                         14000 non-null int64
weight                      14000 non-null int64
admission_type_id           14000 non-null int64
discharge_disposition_id    14000 non-null int64
admission_source_id         14000 non-null int64
time_in_hospital            14000 non-null int64
payer_code                  14000 non-null int64
MED_SPEC_NUM                14000 non-null int64
num_lab_procedures          14000 non-null int64
num_procedures              14000 non-null int64
num_medications             14000 non-null int64
number_outpatient           14000 non-null int64
number_emergency            14000 non-null int64
number_inpatient            14000 non-null int64
DIAG_CAT_1                  14000 non-null int64
DIAG_CAT_2                  14000 non-null int64
DIAG_CAT_3                  14000 non-null int64
number_diagnoses            14000 non-null int64
max_glu_serum               14000 non-null int64
A1Cresult                   14000 non-null int64
metformin                   14000 non-null int64
repaglinide                 14000 non-null int64
nateglinide                 14000 non-null int64
chlorpropamide              14000 non-null int64
glimepiride                 14000 non-null int64
acetohexamide               14000 non-null int64
glipizide                   14000 non-null int64
glyburide                   14000 non-null int64
tolbutamide                 14000 non-null int64
pioglitazone                14000 non-null int64
rosiglitazone               14000 non-null int64
acarbose                    14000 non-null int64
miglitol                    14000 non-null int64
troglitazone                14000 non-null int64
tolazamide                  14000 non-null int64
examide                     14000 non-null int64
citoglipton                 14000 non-null int64
insulin                     14000 non-null int64
glyburide.metformin         14000 non-null int64
glipizide.metformin         14000 non-null int64
glimepiride.pioglitazone    14000 non-null int64
metformin.rosiglitazone     14000 non-null int64
metformin.pioglitazone      14000 non-null int64
change                      14000 non-null int64
diabetesMed                 14000 non-null int64
dtypes: int64(47)
memory usage: 5.0 MB

# Set Y and X

y = df['readm2']
X = df.drop(['readm2'], axis=1)

X.head(12)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 32 columns):
race                        56000 non-null int64
gender                      56000 non-null int64
age                         56000 non-null int64
weight                      56000 non-null int64
admission_type_id           56000 non-null int64
discharge_disposition_id    56000 non-null int64
admission_source_id         56000 non-null int64
time_in_hospital            56000 non-null int64
payer_code                  56000 non-null int64
MED_SPEC_NUM                56000 non-null int64
num_lab_procedures          56000 non-null int64
num_procedures              56000 non-null int64
num_medications             56000 non-null int64
number_outpatient           56000 non-null int64
number_emergency            56000 non-null int64
number_inpatient            56000 non-null int64
DIAG_CAT_1                  56000 non-null int64
DIAG_CAT_2                  56000 non-null int64
DIAG_CAT_3                  56000 non-null int64
number_diagnoses            56000 non-null int64
max_glu_serum               56000 non-null int64
A1Cresult                   56000 non-null int64
metformin                   56000 non-null int64
glimepiride                 56000 non-null int64
glipizide                   56000 non-null int64
glyburide                   56000 non-null int64
pioglitazone                56000 non-null int64
rosiglitazone               56000 non-null int64
insulin                     56000 non-null int64
change                      56000 non-null int64
diabetesMed                 56000 non-null int64
readm2                      56000 non-null int64
dtypes: int64(32)
memory usage: 13.7 MB

score.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14000 entries, 0 to 13999
Data columns (total 47 columns):
race                        14000 non-null int64
gender                      14000 non-null int64
age                         14000 non-null int64
weight                      14000 non-null int64
admission_type_id           14000 non-null int64
discharge_disposition_id    14000 non-null int64
admission_source_id         14000 non-null int64
time_in_hospital            14000 non-null int64
payer_code                  14000 non-null int64
MED_SPEC_NUM                14000 non-null int64
num_lab_procedures          14000 non-null int64
num_procedures              14000 non-null int64
num_medications             14000 non-null int64
number_outpatient           14000 non-null int64
number_emergency            14000 non-null int64
number_inpatient            14000 non-null int64
DIAG_CAT_1                  14000 non-null int64
DIAG_CAT_2                  14000 non-null int64
DIAG_CAT_3                  14000 non-null int64
number_diagnoses            14000 non-null int64
max_glu_serum               14000 non-null int64
A1Cresult                   14000 non-null int64
metformin                   14000 non-null int64
repaglinide                 14000 non-null int64
nateglinide                 14000 non-null int64
chlorpropamide              14000 non-null int64
glimepiride                 14000 non-null int64
acetohexamide               14000 non-null int64
glipizide                   14000 non-null int64
glyburide                   14000 non-null int64
tolbutamide                 14000 non-null int64
pioglitazone                14000 non-null int64
rosiglitazone               14000 non-null int64
acarbose                    14000 non-null int64
miglitol                    14000 non-null int64
troglitazone                14000 non-null int64
tolazamide                  14000 non-null int64
examide                     14000 non-null int64
citoglipton                 14000 non-null int64
insulin                     14000 non-null int64
glyburide.metformin         14000 non-null int64
glipizide.metformin         14000 non-null int64
glimepiride.pioglitazone    14000 non-null int64
metformin.rosiglitazone     14000 non-null int64
metformin.pioglitazone      14000 non-null int64
change                      14000 non-null int64
diabetesMed                 14000 non-null int64
dtypes: int64(47)
memory usage: 5.0 MB

#drop or remove these columns since they are not used in any of the cases
score = score.drop('examide', axis=1)
score = score.drop('citoglipton', axis=1)
score = score.drop('glimepiride.pioglitazone', axis=1)

#drop or remove the column 'ID' since this column is not used in the analysis and disply the result
score = score.drop('acetohexamide', axis=1)
score = score.drop('metformin.pioglitazone', axis=1)
score = score.drop('metformin.rosiglitazone', axis=1)
score = score.drop('tolazamide', axis=1)

score = score.drop('tolbutamide', axis=1)
score = score.drop('troglitazone', axis=1)
score = score.drop('chlorpropamide', axis=1)
score = score.drop('glipizide.metformin', axis=1)
score = score.drop('miglitol', axis=1)
score = score.drop('acarbose', axis=1)
score = score.drop('glyburide.metformin', axis=1)
score = score.drop('nateglinide', axis=1)
score = score.drop('repaglinide', axis=1)

score.head(12)

score.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14000 entries, 0 to 13999
Data columns (total 31 columns):
race                        14000 non-null int64
gender                      14000 non-null int64
age                         14000 non-null int64
weight                      14000 non-null int64
admission_type_id           14000 non-null int64
discharge_disposition_id    14000 non-null int64
admission_source_id         14000 non-null int64
time_in_hospital            14000 non-null int64
payer_code                  14000 non-null int64
MED_SPEC_NUM                14000 non-null int64
num_lab_procedures          14000 non-null int64
num_procedures              14000 non-null int64
num_medications             14000 non-null int64
number_outpatient           14000 non-null int64
number_emergency            14000 non-null int64
number_inpatient            14000 non-null int64
DIAG_CAT_1                  14000 non-null int64
DIAG_CAT_2                  14000 non-null int64
DIAG_CAT_3                  14000 non-null int64
number_diagnoses            14000 non-null int64
max_glu_serum               14000 non-null int64
A1Cresult                   14000 non-null int64
metformin                   14000 non-null int64
glimepiride                 14000 non-null int64
glipizide                   14000 non-null int64
glyburide                   14000 non-null int64
pioglitazone                14000 non-null int64
rosiglitazone               14000 non-null int64
insulin                     14000 non-null int64
change                      14000 non-null int64
diabetesMed                 14000 non-null int64
dtypes: int64(31)
memory usage: 3.3 MB

# develop a random forest model
from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=20)    #building 20 decision trees
clf=clf.fit(X, y)
clf.score(X,y)

0.99219642857142853

Random Forest = 99.23%¶

# generate evaluation metrics
print(metrics.accuracy_score(y, clf.predict(X))) #overall accuracy
print(metrics.confusion_matrix(y, clf.predict(X)))
print(metrics.classification_report(y, clf.predict(X)))

0.992196428571
[[49715     0]
 [  437  5848]]
             precision    recall  f1-score   support

          0       0.99      1.00      1.00     49715
          1       1.00      0.93      0.96      6285

avg / total       0.99      0.99      0.99     56000

print("Features sorted by their rank:")
print(sorted(zip(map(lambda x: round(x, 4), clf.feature_importances_), X.columns)))

Features sorted by their rank:
[(0.0058999999999999999, 'weight'), (0.0071000000000000004, 'max_glu_serum'), (0.0077000000000000002, 'rosiglitazone'), (0.0080999999999999996, 'glimepiride'), (0.0091999999999999998, 'diabetesMed'), (0.0094999999999999998, 'pioglitazone'), (0.0109, 'glyburide'), (0.011900000000000001, 'metformin'), (0.0134, 'glipizide'), (0.0143, 'change'), (0.017000000000000001, 'A1Cresult'), (0.017000000000000001, 'number_emergency'), (0.019, 'gender'), (0.021700000000000001, 'number_outpatient'), (0.023400000000000001, 'race'), (0.023599999999999999, 'admission_source_id'), (0.027099999999999999, 'admission_type_id'), (0.028400000000000002, 'insulin'), (0.035000000000000003, 'number_diagnoses'), (0.0361, 'num_procedures'), (0.037600000000000001, 'discharge_disposition_id'), (0.038600000000000002, 'payer_code'), (0.044400000000000002, 'number_inpatient'), (0.044699999999999997, 'MED_SPEC_NUM'), (0.048599999999999997, 'age'), (0.0591, 'time_in_hospital'), (0.065199999999999994, 'DIAG_CAT_3'), (0.0659, 'DIAG_CAT_1'), (0.070900000000000005, 'DIAG_CAT_2'), (0.081199999999999994, 'num_medications'), (0.097600000000000006, 'num_lab_procedures')]

# another method
pd.DataFrame({'feature':X.columns, 'importance':clf.feature_importances_})

#Predict class probabilities for X
clf.predict_proba(X)

array([[ 0.95,  0.05],
       [ 1.  ,  0.  ],
       [ 0.95,  0.05],
       ..., 
       [ 0.95,  0.05],
       [ 0.95,  0.05],
       [ 0.8 ,  0.2 ]])

Make predictions on the new dataset (scoring dataset without y value)¶

score.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14000 entries, 0 to 13999
Data columns (total 31 columns):
race                        14000 non-null int64
gender                      14000 non-null int64
age                         14000 non-null int64
weight                      14000 non-null int64
admission_type_id           14000 non-null int64
discharge_disposition_id    14000 non-null int64
admission_source_id         14000 non-null int64
time_in_hospital            14000 non-null int64
payer_code                  14000 non-null int64
MED_SPEC_NUM                14000 non-null int64
num_lab_procedures          14000 non-null int64
num_procedures              14000 non-null int64
num_medications             14000 non-null int64
number_outpatient           14000 non-null int64
number_emergency            14000 non-null int64
number_inpatient            14000 non-null int64
DIAG_CAT_1                  14000 non-null int64
DIAG_CAT_2                  14000 non-null int64
DIAG_CAT_3                  14000 non-null int64
number_diagnoses            14000 non-null int64
max_glu_serum               14000 non-null int64
A1Cresult                   14000 non-null int64
metformin                   14000 non-null int64
glimepiride                 14000 non-null int64
glipizide                   14000 non-null int64
glyburide                   14000 non-null int64
pioglitazone                14000 non-null int64
rosiglitazone               14000 non-null int64
insulin                     14000 non-null int64
change                      14000 non-null int64
diabetesMed                 14000 non-null int64
dtypes: int64(31)
memory usage: 3.3 MB

score.head()

#score=pd.read_csv("data/Challenge_1_Validation_Work_Clean_NoString.csv")
output_scoring = clf.predict(score)
predicted_y= pd.DataFrame(output_scoring, columns=['Predicted_Readmit_30'])

probs = clf.predict_proba(score)
probs = pd.DataFrame(probs, columns=['Prob of NO', 'Prob of YES'])

readmit_patients = predicted_y.join(probs)

readmit_patients.to_csv("data/output_readmit_RandomForest_ScoringDataset.csv")

readmit_patients.head()

#finally ...
data1 = score.join(readmit_patients) 
data1.head(10)

	Column Name	Column Value	Type	Description and values
0	encounter_id	Encounter ID	Numeric	Unique identifier of an encounter
1	patient_nbr	Patient number	Numeric	Unique identifier of a patient
2	race	Race	Nominal	Values: Caucasian, Asian, African American, Hi...
3	gender	Gender	Nominal	Values: male, female, and unknown/invalid
4	age	Age	Nominal	Grouped in 10-year intervals: [0, 10), [10, 20...
5	weight	Weight	Numeric	Weight in pounds.
6	admission_type_id	Admission Type	Nominal	Integer identifier corresponding to 9 distinct...
7	discharge_disposition_id	Discharge Disposition	Nominal	Integer identifier corresponding to 29 distinc...
8	admission_source_id	Admission Source	Nominal	Integer identifier corresponding to 21 distinc...
9	time_in_hospital	Time in Hospital	Numeric	Integer number of days between admission and d...
10	payer_code	Payer Code	Nominal	Integer identifier corresponding to 23 distinc...
11	medical_specialty	Medical Specialty	Nominal	Integer identifier of a specialty of the admit...
12	num_lab_procedures	Number of lab procedures	Numeric	Number of lab tests performed during the encou...
13	num_procedures	Number of procedures	Numeric	Number of procedures (other than lab tests) pe...
14	num_medications	Number of medications	Numeric	Number of distinct generic names administered ...
15	number_outpatient	Number of outpatient visits	Numeric	Number of outpatient visits of the patient in ...
16	number_emergency	Number of emergency visits	Numeric	Number of emergency visits of the patient in t...
17	number_inpatient	Number of inpatient visits	Numeric	Number of inpatient visits of the patient in t...
18	diag_1	Diagnosis 1	Nominal	The primary diagnosis (coded as first three di...
19	diag_2	Diagnosis 2	Nominal	Secondary diagnosis (coded as first three digi...
20	diag_3	Diagnosis 3	Nominal	Additional secondary diagnosis (coded as first...
21	number_diagnoses	Number of diagnoses	Numeric	Number of diagnoses entered to the system
22	max_glu_serum	Glucose serum test result	Nominal	Indicates the range of the result or if the te...
23	A1Cresult	A1c test result	Nominal	Indicates the range of the result or if the te...
24	24 features for medications	24 features for medications	Nominal	For the generic names: metformin, repaglinide,...
25	change	Change of medications	Nominal	Indicates if there was a change in diabetic me...
26	diabetesMed	Diabetes medications	Nominal	Indicates if there was any diabetic medication...
27	readmitted	Readmitted	Nominal	Days to inpatient readmission. Values: “<30” i...

	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
race
?	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215	1215
AfricanAmerican	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563	10563
Asian	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356	356
Caucasian	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886	41886
Hispanic	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117	1117
Other	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863	863

	race	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
gender
Female	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990	29990
Male	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010	26010

	race	gender	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
age
[0-10)	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98	98
[10-20)	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355	355
[20-30)	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934	934
[30-40)	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070	2070
[40-50)	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237	5237
[50-60)	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578	9578
[60-70)	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422	12422
[70-80)	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356	14356
[80-90)	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436	9436
[90-100)	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514	1514

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
payer_code
?	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153	22153
BC	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550	2550
CH	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81	81
CM	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044	1044
CP	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405	1405
DM	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309	309
HM	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506	3506
MC	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855	17855
MD	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977	1977
MP	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46
OG	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564	564
OT	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44	44
PO	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312	312
SI	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32	32
SP	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759	2759
UN	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293	1293
WC	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70	70

	encounter_id	patient_nbr	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	medical_specialty	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_inpatient	diag_1	DIAG_CAT_1	diag_2	DIAG_CAT_2	diag_3	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
0	22915332	1475073	Caucasian	Female	[80-90)	?	3	1	4	5	?	?	0	39	3	11	0	0	414	10	289	4	593	18	7	None	None	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	>30
1	158361324	93771396	Caucasian	Female	[70-80)	?	5	3	1	6	MC	?	0	79	1	25	3	0	518	16	428	13	496	16	9	None	None	Steady	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO
2	120453192	24581277	Other	Female	[60-70)	?	1	22	7	4	SP	InternalMedicine	18	29	2	18	0	1	820	24	599	18	191	2	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	NO
3	25590894	5041395	Caucasian	Male	[70-80)	?	1	1	7	3	?	InternalMedicine	18	72	3	18	0	0	537	17	280	4	250.41	3	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	>30
4	154290822	49027563	Caucasian	Female	[30-40)	?	2	1	1	3	?	?	0	21	1	6	0	0	790	23	599	18	V42	32	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO

	encounter_id	patient_nbr	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	medical_specialty	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	DIAG_CAT_1	diag_2	DIAG_CAT_2	diag_3	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed
readmitted
<30	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285
>30	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477	19477
NO	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238	30238

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
weight
>200	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
?	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238	54238
[0-25)	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27	27
[100-125)	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349	349
[125-150)	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67	67
[150-175)	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19
[175-200)	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7
[25-50)	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58
[50-75)	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488	488
[75-100)	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745	745

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
max_glu_serum
>200	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800	800
>300	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692	692
None	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027	53027
Norm	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481	1481

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
A1Cresult
>7	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143	2143
>8	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523	4523
None	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560	46560
Norm	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774	2774

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	diabetesMed	readmitted
change
Ch	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910	25910
No	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090	30090

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	readmitted
diabetesMed
No	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890	12890
Yes	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110	43110

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
MED_SPEC_NUM
0	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562	27562
1	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4
2	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7
3	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12
4	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912	2912
5	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4
6	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4
7	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
8	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189	4189
9	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66	66
10	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
11	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032	4032
12	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318	318
13	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37	37
14	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57	57
15	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118	118
16	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31
17	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23	23
18	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055	8055
19	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914	914
20	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122	122
21	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
22	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15
23	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
24	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361
25	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198	198
26	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17	17
27	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764	764
28	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651	651
29	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24
30	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64	64
31	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7	7
32	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12	12
33	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130	130
34	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
35	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50	50
36	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
37	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92	92
38	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4
39	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15	15
40	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219	219
41	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
42	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56	56
43	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
44	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468	468
45	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
46	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48	48
47	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499	499
48	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600	600
49	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29	29
50	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
51	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19	19
52	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49
53	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359	359
54	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6
55	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703	1703
56	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6
57	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269
58	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
59	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18	18
60	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55	55
61	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287	287
62	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22	22
63	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372	372

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
DIAG_CAT_1
0	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11
1	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72	72
2	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900	1900
3	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311	6311
4	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624	624
5	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233	1233
6	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632	1632
7	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
8	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89	89
9	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824	824
10	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774	5774
11	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292	292
12	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969	1969
13	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766	3766
14	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457	2457
15	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423	1423
16	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797	5797
17	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124	5124
18	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825	2825
19	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367	367
20	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462	1462
21	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677	2677
22	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31	31
23	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273	4273
24	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184	2184
25	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154	154
26	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49
27	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067	1067
28	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684	684
31	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
32	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926	926

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
DIAG_CAT_2
0	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180	180
1	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193	193
2	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392	1392
3	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530	11530
4	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649	1649
5	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436	1436
6	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964	964
8	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188	188
9	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946	3946
10	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984	3984
11	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113
12	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402	4402
13	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719	3719
14	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456	456
15	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781	781
16	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578	5578
17	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198	2198
18	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365	4365
19	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234	234
20	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113	2113
21	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972	972
22	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69	69
23	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635	2635
24	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595	595
25	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11	11
26	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77	77
27	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269	269
28	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540	540
31	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396	396
32	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
DIAG_CAT_3
0	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768	768
1	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196	196
2	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003	1003
3	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633	14633
4	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369	1369
5	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744	1744
6	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106	1106
8	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231	231
9	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087	6087
10	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161	3161
11	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114	114
12	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547	3547
13	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526	2526
14	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374	374
15	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741	741
16	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700	3700
17	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016	2016
18	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532	3532
19	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150	150
20	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491	1491
21	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015	1015
22	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49	49
23	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502	2502
24	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511	511
25	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24	24
26	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159	159
27	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144	144
28	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361	361
31	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689	689
32	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057	2057

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
race	1.000000	0.061706	0.114255	0.040520	0.096587	0.005805	0.033113	-0.020364	0.041640	-0.030777	-0.023193	0.024391	0.022157	0.050845	-0.012812	-0.006053	0.042924	0.029594	0.016000	0.081672	0.054576	-0.013318	0.010548	0.025466	-0.004170	0.006801	0.008261	0.001793	0.018551	0.015784	-0.001455	0.026105	0.005938	0.013237	-0.001307	0.003106	0.003990	NaN	NaN	-0.039862	0.006384	0.005380	NaN	-0.011726	0.001793	0.008300	-0.004537	0.014912
gender	0.061706	1.000000	-0.048579	0.014491	0.014578	-0.019566	-0.005222	-0.031088	0.000833	0.016623	-0.004968	0.061668	-0.023819	-0.005846	-0.024202	-0.013405	-0.034311	0.008083	0.008343	-0.007818	-0.001347	0.016539	0.001549	-0.004777	-0.005390	0.006481	-0.000156	-0.003935	0.026810	0.034631	-0.001727	0.002339	0.010843	0.010581	0.009920	0.007860	0.003242	NaN	NaN	0.000247	0.002489	0.007965	NaN	0.004538	-0.003935	0.012476	0.015391	-0.013626
age	0.114255	-0.048579	1.000000	0.005716	-0.005747	0.113970	0.041070	0.107273	0.058032	-0.068202	0.025665	-0.028360	0.039010	0.029064	-0.089149	-0.047012	0.091837	0.077541	0.052021	0.243515	0.018618	-0.147559	-0.060696	0.045565	0.020363	0.012367	0.044360	0.002400	0.055867	0.076798	0.010110	0.013860	0.003034	0.008092	0.011788	-0.001978	0.003605	NaN	NaN	-0.079078	-0.002451	0.003658	NaN	0.002400	-0.000257	-0.037793	-0.025360	0.029704
weight	0.040520	0.014491	0.005716	1.000000	0.037503	-0.035383	0.003026	0.023652	0.047819	0.004630	0.090456	0.018693	0.011274	0.104440	0.003706	-0.009154	0.023982	0.031824	0.014000	0.054391	-0.037139	-0.021109	0.007304	-0.005440	0.010707	-0.000839	0.013694	-0.000736	0.017062	0.008707	-0.002326	0.026059	0.004232	0.010411	-0.003532	-0.001274	0.000692	NaN	NaN	-0.076697	-0.014159	-0.002207	NaN	-0.000736	-0.000736	-0.041219	-0.030585	0.027236
admission_type_id	0.096587	0.014578	-0.005747	0.037503	1.000000	0.085986	0.098007	-0.014285	-0.136863	0.185351	-0.145869	0.131923	0.075711	0.030746	-0.018190	-0.032648	0.032151	-0.005648	-0.008918	-0.113991	0.352793	-0.043929	0.008631	-0.003481	-0.008099	0.007875	-0.003178	-0.002988	0.007991	-0.002804	0.006347	0.018570	0.022930	0.006061	-0.001414	0.003307	0.010291	NaN	NaN	-0.025368	-0.000573	-0.005046	NaN	-0.002988	0.002888	0.003992	-0.003930	-0.008561
discharge_disposition_id	0.005805	-0.019566	0.113970	-0.035383	0.085986	1.000000	0.016614	0.161954	-0.123220	-0.024028	0.022906	0.015536	0.105415	-0.006101	-0.024692	0.019240	0.034616	0.029774	0.024778	0.049496	0.037086	-0.020713	-0.008376	-0.002759	-0.008790	0.018525	-0.022360	0.014597	-0.013379	0.048256	0.003228	-0.014116	-0.001694	0.006779	0.005779	0.008684	0.013139	NaN	NaN	-0.041842	-0.002994	0.000933	NaN	-0.002174	-0.000576	-0.014047	-0.029452	0.009300
admission_source_id	0.033113	-0.005222	0.041070	0.003026	0.098007	0.016614	1.000000	-0.006996	-0.100157	-0.152760	0.046823	-0.137044	-0.055016	0.028833	0.061938	0.033697	-0.007753	-0.019796	0.001447	0.076318	0.412356	0.006512	-0.033283	-0.003732	-0.019612	0.002666	-0.026685	0.001296	0.009300	0.004919	0.001791	-0.005729	-0.008894	-0.000753	-0.000763	0.002245	0.001834	NaN	NaN	0.005094	-0.024616	-0.000281	NaN	0.001296	-0.004958	0.002583	0.000535	0.030377
time_in_hospital	-0.020364	-0.031088	0.107273	0.023652	-0.014285	0.161954	-0.006996	1.000000	-0.037805	0.023146	0.318234	0.193139	0.468752	-0.003410	-0.005467	0.079929	-0.019913	0.086503	0.068677	0.224265	0.029079	0.058088	-0.009071	0.034985	0.003320	0.004094	0.016086	0.013596	0.016737	0.023482	0.001799	0.008521	0.008531	0.007231	0.005083	0.004746	0.000328	NaN	NaN	0.101223	-0.006358	-0.001692	NaN	-0.003396	0.002268	0.112359	0.059464	0.057129
payer_code	0.041640	0.000833	0.058032	0.047819	-0.136863	-0.123220	-0.100157	-0.037805	1.000000	-0.082746	-0.049680	-0.047581	0.005658	0.062572	0.067316	0.009598	0.008458	0.036335	0.033135	0.076424	-0.095739	-0.006824	0.027596	0.032986	0.014676	-0.022046	0.038055	-0.004231	0.005875	-0.047599	-0.002662	0.034867	-0.008782	-0.002629	0.011455	-0.007329	-0.015677	NaN	NaN	0.115265	0.055730	0.010871	NaN	0.009326	-0.000358	0.121010	0.077597	0.004353
MED_SPEC_NUM	-0.030777	0.016623	-0.068202	0.004630	0.185351	-0.024028	-0.152760	0.023146	-0.082746	1.000000	-0.068863	0.076952	0.036943	-0.051445	-0.009879	-0.013909	0.018820	-0.019354	-0.015192	-0.176693	-0.003316	-0.009813	0.023068	0.010220	0.006590	0.002161	0.012798	-0.002891	0.007273	-0.005929	-0.001944	0.002210	0.016639	-0.005808	0.002816	-0.002660	-0.004689	NaN	NaN	-0.014342	0.000051	-0.006234	NaN	-0.002891	0.010115	-0.005111	-0.002299	-0.044800
num_lab_procedures	-0.023193	-0.004968	0.025665	0.090456	-0.145869	0.022906	0.046823	0.318234	-0.049680	-0.068863	1.000000	0.055081	0.267707	-0.008437	0.000613	0.037763	-0.071046	0.011204	0.011021	0.149116	-0.124907	0.236383	-0.044042	0.010438	-0.008292	-0.005659	0.005344	0.005344	0.012450	-0.001768	-0.001320	-0.015599	-0.010260	-0.000654	-0.002963	0.005036	0.000008	NaN	NaN	0.085401	-0.010852	-0.006685	NaN	0.001689	-0.004330	0.062801	0.030903	0.035997
num_procedures	0.024391	0.061668	-0.028360	0.018693	0.131923	0.015536	-0.137044	0.193139	-0.047581	0.076952	0.055081	1.000000	0.387685	-0.028257	-0.033659	-0.061114	-0.056866	0.036607	0.025920	0.074394	-0.069910	-0.017477	-0.038122	0.005662	-0.002359	0.004757	0.007223	0.006615	0.004999	0.001531	-0.003423	0.016471	0.018742	-0.000362	-0.001521	-0.005745	0.005154	NaN	NaN	0.015020	-0.000553	-0.006640	NaN	-0.003317	-0.000834	0.005976	-0.009904	-0.037714
num_medications	0.022157	-0.023819	0.039010	0.011274	0.075711	0.105415	-0.055016	0.468752	0.005658	0.036943	0.267707	0.387685	1.000000	0.047313	0.017129	0.066793	0.004288	0.084268	0.063166	0.263311	0.001639	0.013044	0.069433	0.019283	0.023352	-0.000940	0.045223	0.009348	0.056985	0.030886	0.002943	0.071584	0.052860	0.017947	0.006422	0.002992	-0.002113	NaN	NaN	0.198963	0.013382	0.002757	NaN	-0.002603	0.002074	0.248529	0.186247	0.050711
number_outpatient	0.050845	-0.005846	0.029064	0.104440	0.030746	-0.006101	0.028833	-0.003410	0.062572	-0.051445	-0.008437	-0.028257	0.047313	1.000000	0.087824	0.103471	-0.009347	0.028015	0.026595	0.093518	0.054949	-0.024324	-0.013006	0.001026	0.002719	-0.004402	-0.009039	-0.001242	0.010527	-0.000482	0.000350	0.012212	-0.001550	0.009388	-0.002243	-0.002152	-0.005556	NaN	NaN	0.010029	-0.008428	0.003037	NaN	-0.001242	-0.001242	0.027105	0.017340	0.068145
number_emergency	-0.012812	-0.024202	-0.089149	0.003706	-0.018190	-0.024692	0.061938	-0.005467	0.067316	-0.009879	0.000613	-0.033659	0.017129	0.087824	1.000000	0.279626	-0.023803	-0.004155	0.007427	0.059398	0.035679	-0.004270	-0.009572	0.007820	0.005489	-0.004218	0.003318	-0.000907	-0.003426	-0.027870	-0.002870	-0.001978	-0.006844	0.004224	-0.000207	-0.001572	-0.004059	NaN	NaN	0.048501	0.001956	-0.002723	NaN	-0.000907	-0.000907	0.041797	0.029415	0.103321
number_inpatient	-0.006053	-0.013405	-0.047012	-0.009154	-0.032648	0.019240	0.033697	0.079929	0.009598	-0.013909	0.037763	-0.061114	0.066793	0.103471	0.279626	1.000000	-0.004620	0.024244	0.032150	0.102473	0.038503	-0.049379	-0.073780	0.011936	-0.006284	-0.008317	-0.016545	-0.002118	-0.022736	-0.036659	-0.003545	-0.026804	-0.021471	0.000411	-0.003851	-0.003669	-0.003526	NaN	NaN	0.060505	-0.008426	-0.000813	NaN	0.001207	-0.002118	0.025420	0.025559	0.233149
DIAG_CAT_1	0.042924	-0.034311	0.091837	0.023982	0.032151	0.034616	-0.007753	-0.019913	0.008458	0.018820	-0.071046	-0.056866	0.004288	-0.009347	-0.023803	-0.004620	1.000000	0.025858	0.028021	0.046451	-0.016030	-0.091392	0.033199	0.002242	-0.000440	-0.002017	0.000410	0.001038	0.010541	0.017872	0.006039	0.024890	0.010041	0.003061	0.006030	0.000456	0.000745	NaN	NaN	-0.075260	0.015281	0.004664	NaN	-0.003029	0.003943	-0.033688	-0.028985	-0.004994
DIAG_CAT_2	0.029594	0.008083	0.077541	0.031824	-0.005648	0.029774	-0.019796	0.086503	0.036335	-0.019354	0.011204	0.036607	0.084268	0.028015	-0.004155	0.024244	0.025858	1.000000	0.081391	0.171521	-0.017962	-0.044930	-0.018313	0.003082	-0.000322	-0.004128	0.006773	0.002264	0.004223	0.010435	0.000339	0.000030	-0.010618	0.000704	0.005705	0.001300	-0.003710	NaN	NaN	-0.007776	-0.007621	0.005659	NaN	-0.000006	-0.005116	-0.006439	-0.010210	0.011850
DIAG_CAT_3	0.016000	0.008343	0.052021	0.014000	-0.008918	0.024778	0.001447	0.068677	0.033135	-0.015192	0.011021	0.025920	0.063166	0.026595	0.007427	0.032150	0.028021	0.081391	1.000000	0.186667	-0.009693	-0.031716	-0.024179	0.005636	0.003922	-0.007445	-0.010677	0.000333	-0.005554	-0.005157	-0.002879	-0.008180	-0.003303	0.000458	-0.000319	-0.000620	-0.003145	NaN	NaN	0.013942	-0.000101	0.006007	NaN	0.006032	-0.004330	0.005824	-0.007452	0.027877
number_diagnoses	0.081672	-0.007818	0.243515	0.054391	-0.113991	0.049496	0.076318	0.224265	0.076424	-0.176693	0.149116	0.074394	0.263311	0.093518	0.059398	0.102473	0.046451	0.171521	0.186667	1.000000	-0.036161	-0.032983	-0.073736	0.033225	0.012336	-0.014080	0.013640	0.003449	-0.005975	-0.024247	0.001220	0.002278	-0.011524	0.007741	-0.000293	0.004710	-0.013444	NaN	NaN	0.076730	-0.005894	-0.006428	NaN	0.003449	-0.007491	0.055250	0.019375	0.103885
max_glu_serum	0.054576	-0.001347	0.018618	-0.037139	0.352793	0.037086	0.412356	0.029079	-0.095739	-0.003316	-0.124907	-0.069910	0.001639	0.054949	0.035679	0.038503	-0.016030	-0.017962	-0.009693	-0.036161	1.000000	-0.043540	-0.029790	-0.015106	-0.016794	0.008938	-0.031840	-0.000902	0.005931	0.000373	0.006437	-0.014531	-0.009275	0.005479	-0.004328	-0.001562	-0.004032	NaN	NaN	0.000884	-0.014296	-0.002705	NaN	-0.000902	-0.000902	0.008958	-0.005206	0.017684
A1Cresult	-0.013318	0.016539	-0.147559	-0.021109	-0.043929	-0.020713	0.006512	0.058088	-0.006824	-0.009813	0.236383	-0.017477	0.013044	-0.024324	-0.004270	-0.049379	-0.091392	-0.044930	-0.031716	-0.032983	-0.043540	1.000000	0.051894	0.022541	-0.000669	-0.003225	0.022787	-0.001747	0.020844	0.009977	-0.005526	0.000223	0.009548	0.009374	0.007741	-0.003026	-0.000390	NaN	NaN	0.107227	-0.005008	0.001082	NaN	-0.001747	-0.001747	0.105614	0.086291	-0.013614
metformin	0.010548	0.001549	-0.060696	0.007304	0.008631	-0.008376	-0.033283	-0.009071	0.027596	0.023068	-0.044042	-0.038122	0.069433	-0.013006	-0.009572	-0.073780	0.033199	-0.018313	-0.024179	-0.073736	-0.029790	0.051894	1.000000	-0.001074	0.020372	-0.011841	0.047475	-0.002068	0.077111	0.129061	-0.006539	0.060566	0.097708	0.006246	0.005628	-0.003582	0.004664	NaN	NaN	-0.017392	-0.021191	-0.002748	NaN	0.008300	0.003116	0.325302	0.267566	-0.035809
repaglinide	0.025466	-0.004777	0.045565	-0.005440	-0.003481	-0.002759	-0.003732	0.034985	0.032986	0.010220	0.010438	0.005662	0.019283	0.001026	0.007820	0.011936	0.002242	0.003082	0.005636	0.033225	-0.015106	0.022541	-0.001074	1.000000	-0.003246	-0.003466	-0.007518	-0.000511	-0.015927	-0.024160	-0.001617	0.019393	0.009031	0.011257	0.018066	-0.000886	-0.002287	NaN	NaN	0.006058	-0.004506	-0.001534	NaN	-0.000511	-0.000511	0.071294	0.066174	0.014286
nateglinide	-0.004170	-0.005390	0.020363	0.010707	-0.008099	-0.008790	-0.019612	0.003320	0.014676	0.006590	-0.008292	-0.002359	0.023352	0.002719	0.005489	-0.006284	-0.000440	-0.000322	0.003922	0.012336	-0.016794	-0.000669	0.020372	-0.003246	1.000000	-0.002386	0.004488	-0.000352	-0.018191	-0.020817	-0.001113	0.025830	0.013947	-0.004585	0.018302	-0.000610	-0.001575	NaN	NaN	0.001396	-0.006775	-0.001056	NaN	-0.000352	-0.000352	0.052927	0.045552	0.007164
chlorpropamide	0.006801	0.006481	0.012367	-0.000839	0.007875	0.018525	0.002666	0.004094	-0.022046	0.002161	-0.005659	0.004757	-0.000940	-0.004402	-0.004218	-0.008317	-0.002017	-0.004128	-0.007445	-0.014080	0.008938	-0.003225	-0.011841	-0.003466	-0.002386	1.000000	-0.006537	-0.000121	-0.010745	-0.005823	-0.000383	-0.007959	-0.000123	-0.001576	-0.000581	-0.000210	-0.000541	NaN	NaN	-0.020008	-0.002329	-0.000363	NaN	-0.000121	-0.000121	-0.007035	0.015661	-0.002806
glimepiride	0.008261	-0.000156	0.044360	0.013694	-0.003178	-0.022360	-0.026685	0.016086	0.038055	0.012798	0.005344	0.007223	0.045223	-0.009039	0.003318	-0.016545	0.000410	0.006773	-0.010677	0.013640	-0.031840	0.022787	0.047475	-0.007518	0.004488	-0.006537	1.000000	-0.000964	-0.071983	-0.067334	-0.003050	0.042601	0.038655	0.018418	0.019830	0.009191	-0.004314	NaN	NaN	0.012479	-0.012202	-0.002894	NaN	-0.000964	-0.000964	0.138970	0.124797	0.004760
acetohexamide	0.001793	-0.003935	0.002400	-0.000736	-0.002988	0.014597	0.001296	0.013596	-0.004231	-0.002891	0.005344	0.006615	0.009348	-0.001242	-0.000907	-0.002118	0.001038	0.002264	0.000333	0.003449	-0.000902	-0.001747	-0.002068	-0.000511	-0.000352	-0.000121	-0.000964	1.000000	-0.001585	-0.001414	-0.000056	-0.001174	-0.001085	-0.000233	-0.000086	-0.000031	-0.000080	NaN	NaN	0.003607	-0.000344	-0.000054	NaN	-0.000018	-0.000018	0.004554	0.002311	0.002639
glipizide	0.018551	0.026810	0.055867	0.017062	0.007991	-0.013379	0.009300	0.016737	0.005875	0.007273	0.012450	0.004999	0.056985	0.010527	-0.003426	-0.022736	0.010541	0.004223	-0.005554	-0.005975	0.005931	0.020844	0.077111	-0.015927	-0.018191	-0.010745	-0.071983	-0.001585	1.000000	-0.104495	-0.005014	0.049752	0.041498	0.030598	0.002971	-0.002746	-0.001524	NaN	NaN	-0.027179	-0.027923	-0.000607	NaN	-0.001585	-0.001585	0.194260	0.205145	0.014766
glyburide	0.015784	0.034631	0.076798	0.008707	-0.002804	0.048256	0.004919	0.023482	-0.047599	-0.005929	-0.001768	0.001531	0.030886	-0.000482	-0.027870	-0.036659	0.017872	0.010435	-0.005157	-0.024247	0.000373	0.009977	0.129061	-0.024160	-0.020817	-0.005823	-0.067334	-0.001414	-0.104495	1.000000	-0.004473	0.027727	0.030766	0.015094	-0.000056	-0.002450	-0.006327	NaN	NaN	-0.071853	-0.006909	0.000245	NaN	-0.001414	-0.001414	0.172392	0.183024	-0.004492
tolbutamide	-0.001455	-0.001727	0.010110	-0.002326	0.006347	0.003228	0.001791	0.001799	-0.002662	-0.001944	-0.001320	-0.003423	0.002943	0.000350	-0.002870	-0.003545	0.006039	0.000339	-0.002879	0.001220	0.006437	-0.005526	-0.006539	-0.001617	-0.001113	-0.000383	-0.003050	-0.000056	-0.005014	-0.004473	1.000000	-0.003714	-0.003432	-0.000736	-0.000271	-0.000098	-0.000253	NaN	NaN	-0.001925	-0.001087	-0.000169	NaN	-0.000056	-0.000056	0.001000	0.007308	-0.007263
pioglitazone	0.026105	0.002339	0.013860	0.026059	0.018570	-0.014116	-0.005729	0.008521	0.034867	0.002210	-0.015599	0.016471	0.071584	0.012212	-0.001978	-0.026804	0.024890	0.000030	-0.008180	0.002278	-0.014531	0.000223	0.060566	0.019393	0.025830	-0.007959	0.042601	-0.001174	0.049752	0.027727	-0.003714	1.000000	-0.062763	0.015377	0.000791	-0.002034	-0.001659	NaN	NaN	0.003954	0.022117	0.007190	NaN	-0.001174	0.014894	0.203180	0.151949	0.011002
rosiglitazone	0.005938	0.010843	0.003034	0.004232	0.022930	-0.001694	-0.008894	0.008531	-0.008782	0.016639	-0.010260	0.018742	0.052860	-0.001550	-0.006844	-0.021471	0.010041	-0.010618	-0.003303	-0.011524	-0.009275	0.009548	0.097708	0.009031	0.013947	-0.000123	0.038655	-0.001085	0.041498	0.030766	-0.003432	-0.062763	1.000000	0.002006	0.003416	0.008079	-0.000996	NaN	NaN	0.004080	0.003340	-0.003256	NaN	-0.001085	-0.001085	0.191641	0.140410	0.005522
acarbose	0.013237	0.010581	0.008092	0.010411	0.006061	0.006779	-0.000753	0.007231	-0.002629	-0.005808	-0.000654	-0.000362	0.017947	0.009388	0.004224	0.000411	0.003061	0.000704	0.000458	0.007741	0.005479	0.009374	0.006246	0.011257	-0.004585	-0.001576	0.018418	-0.000233	0.030598	0.015094	-0.000736	0.015377	0.002006	1.000000	-0.001117	-0.000403	-0.001040	NaN	NaN	-0.001790	0.013046	-0.000698	NaN	-0.000233	-0.000233	0.047261	0.030097	0.007816
miglitol	-0.001307	0.009920	0.011788	-0.003532	-0.001414	0.005779	-0.000763	0.005083	0.011455	0.002816	-0.002963	-0.001521	0.006422	-0.002243	-0.000207	-0.003851	0.006030	0.005705	-0.000319	-0.000293	-0.004328	0.007741	0.005628	0.018066	0.018302	-0.000581	0.019830	-0.000086	0.002971	-0.000056	-0.000271	0.000791	0.003416	-0.001117	1.000000	-0.000148	-0.000383	NaN	NaN	0.000451	-0.001650	-0.000257	NaN	-0.000086	-0.000086	0.018472	0.011094	0.003413
troglitazone	0.003106	0.007860	-0.001978	-0.001274	0.003307	0.008684	0.002245	0.004746	-0.007329	-0.002660	0.005036	-0.005745	0.002992	-0.002152	-0.001572	-0.003669	0.000456	0.001300	-0.000620	0.004710	-0.001562	-0.003026	-0.003582	-0.000886	-0.000610	-0.000210	0.009191	-0.000031	-0.002746	-0.002450	-0.000098	-0.002034	0.008079	-0.000403	-0.000148	1.000000	-0.000138	NaN	NaN	-0.000391	-0.000595	-0.000093	NaN	-0.000031	-0.000031	0.007888	0.004002	0.001009
tolazamide	0.003990	0.003242	0.003605	0.000692	0.010291	0.013139	0.001834	0.000328	-0.015677	-0.004689	0.000008	0.005154	-0.002113	-0.005556	-0.004059	-0.003526	0.000745	-0.003710	-0.003145	-0.013444	-0.004032	-0.000390	0.004664	-0.002287	-0.001575	-0.000541	-0.004314	-0.000080	-0.001524	-0.006327	-0.000253	-0.001659	-0.000996	-0.001040	-0.000383	-0.000138	1.000000	NaN	NaN	-0.013867	-0.001537	-0.000240	NaN	-0.000080	-0.000080	-0.002376	0.010336	-0.007513
examide	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
citoglipton	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
insulin	-0.039862	0.000247	-0.079078	-0.076697	-0.025368	-0.041842	0.005094	0.101223	0.115265	-0.014342	0.085401	0.015020	0.198963	0.010029	0.048501	0.060505	-0.075260	-0.007776	0.013942	0.076730	0.000884	0.107227	-0.017392	0.006058	0.001396	-0.020008	0.012479	0.003607	-0.027179	-0.071853	-0.001925	0.003954	0.004080	-0.001790	0.000451	-0.000391	-0.013867	NaN	NaN	1.000000	0.005828	-0.000677	NaN	0.003607	0.003607	0.461502	0.525169	0.040750

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide.metformin	glipizide.metformin	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
race	1.000000	0.061706	0.114255	0.040520	0.096587	0.005805	0.033113	-0.020364	0.041640	-0.030777	-0.023193	0.024391	0.022157	0.050845	-0.012812	-0.006053	0.042924	0.029594	0.016000	0.081672	0.054576	-0.013318	0.010548	0.025466	-0.004170	0.006801	0.008261	0.001793	0.018551	0.015784	-0.001455	0.026105	0.005938	0.013237	-0.001307	0.003106	0.003990	-0.039862	0.006384	0.005380	-0.011726	0.001793	0.008300	-0.004537	0.014912
gender	0.061706	1.000000	-0.048579	0.014491	0.014578	-0.019566	-0.005222	-0.031088	0.000833	0.016623	-0.004968	0.061668	-0.023819	-0.005846	-0.024202	-0.013405	-0.034311	0.008083	0.008343	-0.007818	-0.001347	0.016539	0.001549	-0.004777	-0.005390	0.006481	-0.000156	-0.003935	0.026810	0.034631	-0.001727	0.002339	0.010843	0.010581	0.009920	0.007860	0.003242	0.000247	0.002489	0.007965	0.004538	-0.003935	0.012476	0.015391	-0.013626
age	0.114255	-0.048579	1.000000	0.005716	-0.005747	0.113970	0.041070	0.107273	0.058032	-0.068202	0.025665	-0.028360	0.039010	0.029064	-0.089149	-0.047012	0.091837	0.077541	0.052021	0.243515	0.018618	-0.147559	-0.060696	0.045565	0.020363	0.012367	0.044360	0.002400	0.055867	0.076798	0.010110	0.013860	0.003034	0.008092	0.011788	-0.001978	0.003605	-0.079078	-0.002451	0.003658	0.002400	-0.000257	-0.037793	-0.025360	0.029704
weight	0.040520	0.014491	0.005716	1.000000	0.037503	-0.035383	0.003026	0.023652	0.047819	0.004630	0.090456	0.018693	0.011274	0.104440	0.003706	-0.009154	0.023982	0.031824	0.014000	0.054391	-0.037139	-0.021109	0.007304	-0.005440	0.010707	-0.000839	0.013694	-0.000736	0.017062	0.008707	-0.002326	0.026059	0.004232	0.010411	-0.003532	-0.001274	0.000692	-0.076697	-0.014159	-0.002207	-0.000736	-0.000736	-0.041219	-0.030585	0.027236
admission_type_id	0.096587	0.014578	-0.005747	0.037503	1.000000	0.085986	0.098007	-0.014285	-0.136863	0.185351	-0.145869	0.131923	0.075711	0.030746	-0.018190	-0.032648	0.032151	-0.005648	-0.008918	-0.113991	0.352793	-0.043929	0.008631	-0.003481	-0.008099	0.007875	-0.003178	-0.002988	0.007991	-0.002804	0.006347	0.018570	0.022930	0.006061	-0.001414	0.003307	0.010291	-0.025368	-0.000573	-0.005046	-0.002988	0.002888	0.003992	-0.003930	-0.008561
discharge_disposition_id	0.005805	-0.019566	0.113970	-0.035383	0.085986	1.000000	0.016614	0.161954	-0.123220	-0.024028	0.022906	0.015536	0.105415	-0.006101	-0.024692	0.019240	0.034616	0.029774	0.024778	0.049496	0.037086	-0.020713	-0.008376	-0.002759	-0.008790	0.018525	-0.022360	0.014597	-0.013379	0.048256	0.003228	-0.014116	-0.001694	0.006779	0.005779	0.008684	0.013139	-0.041842	-0.002994	0.000933	-0.002174	-0.000576	-0.014047	-0.029452	0.009300
admission_source_id	0.033113	-0.005222	0.041070	0.003026	0.098007	0.016614	1.000000	-0.006996	-0.100157	-0.152760	0.046823	-0.137044	-0.055016	0.028833	0.061938	0.033697	-0.007753	-0.019796	0.001447	0.076318	0.412356	0.006512	-0.033283	-0.003732	-0.019612	0.002666	-0.026685	0.001296	0.009300	0.004919	0.001791	-0.005729	-0.008894	-0.000753	-0.000763	0.002245	0.001834	0.005094	-0.024616	-0.000281	0.001296	-0.004958	0.002583	0.000535	0.030377
time_in_hospital	-0.020364	-0.031088	0.107273	0.023652	-0.014285	0.161954	-0.006996	1.000000	-0.037805	0.023146	0.318234	0.193139	0.468752	-0.003410	-0.005467	0.079929	-0.019913	0.086503	0.068677	0.224265	0.029079	0.058088	-0.009071	0.034985	0.003320	0.004094	0.016086	0.013596	0.016737	0.023482	0.001799	0.008521	0.008531	0.007231	0.005083	0.004746	0.000328	0.101223	-0.006358	-0.001692	-0.003396	0.002268	0.112359	0.059464	0.057129
payer_code	0.041640	0.000833	0.058032	0.047819	-0.136863	-0.123220	-0.100157	-0.037805	1.000000	-0.082746	-0.049680	-0.047581	0.005658	0.062572	0.067316	0.009598	0.008458	0.036335	0.033135	0.076424	-0.095739	-0.006824	0.027596	0.032986	0.014676	-0.022046	0.038055	-0.004231	0.005875	-0.047599	-0.002662	0.034867	-0.008782	-0.002629	0.011455	-0.007329	-0.015677	0.115265	0.055730	0.010871	0.009326	-0.000358	0.121010	0.077597	0.004353
MED_SPEC_NUM	-0.030777	0.016623	-0.068202	0.004630	0.185351	-0.024028	-0.152760	0.023146	-0.082746	1.000000	-0.068863	0.076952	0.036943	-0.051445	-0.009879	-0.013909	0.018820	-0.019354	-0.015192	-0.176693	-0.003316	-0.009813	0.023068	0.010220	0.006590	0.002161	0.012798	-0.002891	0.007273	-0.005929	-0.001944	0.002210	0.016639	-0.005808	0.002816	-0.002660	-0.004689	-0.014342	0.000051	-0.006234	-0.002891	0.010115	-0.005111	-0.002299	-0.044800
num_lab_procedures	-0.023193	-0.004968	0.025665	0.090456	-0.145869	0.022906	0.046823	0.318234	-0.049680	-0.068863	1.000000	0.055081	0.267707	-0.008437	0.000613	0.037763	-0.071046	0.011204	0.011021	0.149116	-0.124907	0.236383	-0.044042	0.010438	-0.008292	-0.005659	0.005344	0.005344	0.012450	-0.001768	-0.001320	-0.015599	-0.010260	-0.000654	-0.002963	0.005036	0.000008	0.085401	-0.010852	-0.006685	0.001689	-0.004330	0.062801	0.030903	0.035997
num_procedures	0.024391	0.061668	-0.028360	0.018693	0.131923	0.015536	-0.137044	0.193139	-0.047581	0.076952	0.055081	1.000000	0.387685	-0.028257	-0.033659	-0.061114	-0.056866	0.036607	0.025920	0.074394	-0.069910	-0.017477	-0.038122	0.005662	-0.002359	0.004757	0.007223	0.006615	0.004999	0.001531	-0.003423	0.016471	0.018742	-0.000362	-0.001521	-0.005745	0.005154	0.015020	-0.000553	-0.006640	-0.003317	-0.000834	0.005976	-0.009904	-0.037714
num_medications	0.022157	-0.023819	0.039010	0.011274	0.075711	0.105415	-0.055016	0.468752	0.005658	0.036943	0.267707	0.387685	1.000000	0.047313	0.017129	0.066793	0.004288	0.084268	0.063166	0.263311	0.001639	0.013044	0.069433	0.019283	0.023352	-0.000940	0.045223	0.009348	0.056985	0.030886	0.002943	0.071584	0.052860	0.017947	0.006422	0.002992	-0.002113	0.198963	0.013382	0.002757	-0.002603	0.002074	0.248529	0.186247	0.050711
number_outpatient	0.050845	-0.005846	0.029064	0.104440	0.030746	-0.006101	0.028833	-0.003410	0.062572	-0.051445	-0.008437	-0.028257	0.047313	1.000000	0.087824	0.103471	-0.009347	0.028015	0.026595	0.093518	0.054949	-0.024324	-0.013006	0.001026	0.002719	-0.004402	-0.009039	-0.001242	0.010527	-0.000482	0.000350	0.012212	-0.001550	0.009388	-0.002243	-0.002152	-0.005556	0.010029	-0.008428	0.003037	-0.001242	-0.001242	0.027105	0.017340	0.068145
number_emergency	-0.012812	-0.024202	-0.089149	0.003706	-0.018190	-0.024692	0.061938	-0.005467	0.067316	-0.009879	0.000613	-0.033659	0.017129	0.087824	1.000000	0.279626	-0.023803	-0.004155	0.007427	0.059398	0.035679	-0.004270	-0.009572	0.007820	0.005489	-0.004218	0.003318	-0.000907	-0.003426	-0.027870	-0.002870	-0.001978	-0.006844	0.004224	-0.000207	-0.001572	-0.004059	0.048501	0.001956	-0.002723	-0.000907	-0.000907	0.041797	0.029415	0.103321
number_inpatient	-0.006053	-0.013405	-0.047012	-0.009154	-0.032648	0.019240	0.033697	0.079929	0.009598	-0.013909	0.037763	-0.061114	0.066793	0.103471	0.279626	1.000000	-0.004620	0.024244	0.032150	0.102473	0.038503	-0.049379	-0.073780	0.011936	-0.006284	-0.008317	-0.016545	-0.002118	-0.022736	-0.036659	-0.003545	-0.026804	-0.021471	0.000411	-0.003851	-0.003669	-0.003526	0.060505	-0.008426	-0.000813	0.001207	-0.002118	0.025420	0.025559	0.233149
DIAG_CAT_1	0.042924	-0.034311	0.091837	0.023982	0.032151	0.034616	-0.007753	-0.019913	0.008458	0.018820	-0.071046	-0.056866	0.004288	-0.009347	-0.023803	-0.004620	1.000000	0.025858	0.028021	0.046451	-0.016030	-0.091392	0.033199	0.002242	-0.000440	-0.002017	0.000410	0.001038	0.010541	0.017872	0.006039	0.024890	0.010041	0.003061	0.006030	0.000456	0.000745	-0.075260	0.015281	0.004664	-0.003029	0.003943	-0.033688	-0.028985	-0.004994
DIAG_CAT_2	0.029594	0.008083	0.077541	0.031824	-0.005648	0.029774	-0.019796	0.086503	0.036335	-0.019354	0.011204	0.036607	0.084268	0.028015	-0.004155	0.024244	0.025858	1.000000	0.081391	0.171521	-0.017962	-0.044930	-0.018313	0.003082	-0.000322	-0.004128	0.006773	0.002264	0.004223	0.010435	0.000339	0.000030	-0.010618	0.000704	0.005705	0.001300	-0.003710	-0.007776	-0.007621	0.005659	-0.000006	-0.005116	-0.006439	-0.010210	0.011850
DIAG_CAT_3	0.016000	0.008343	0.052021	0.014000	-0.008918	0.024778	0.001447	0.068677	0.033135	-0.015192	0.011021	0.025920	0.063166	0.026595	0.007427	0.032150	0.028021	0.081391	1.000000	0.186667	-0.009693	-0.031716	-0.024179	0.005636	0.003922	-0.007445	-0.010677	0.000333	-0.005554	-0.005157	-0.002879	-0.008180	-0.003303	0.000458	-0.000319	-0.000620	-0.003145	0.013942	-0.000101	0.006007	0.006032	-0.004330	0.005824	-0.007452	0.027877
number_diagnoses	0.081672	-0.007818	0.243515	0.054391	-0.113991	0.049496	0.076318	0.224265	0.076424	-0.176693	0.149116	0.074394	0.263311	0.093518	0.059398	0.102473	0.046451	0.171521	0.186667	1.000000	-0.036161	-0.032983	-0.073736	0.033225	0.012336	-0.014080	0.013640	0.003449	-0.005975	-0.024247	0.001220	0.002278	-0.011524	0.007741	-0.000293	0.004710	-0.013444	0.076730	-0.005894	-0.006428	0.003449	-0.007491	0.055250	0.019375	0.103885
max_glu_serum	0.054576	-0.001347	0.018618	-0.037139	0.352793	0.037086	0.412356	0.029079	-0.095739	-0.003316	-0.124907	-0.069910	0.001639	0.054949	0.035679	0.038503	-0.016030	-0.017962	-0.009693	-0.036161	1.000000	-0.043540	-0.029790	-0.015106	-0.016794	0.008938	-0.031840	-0.000902	0.005931	0.000373	0.006437	-0.014531	-0.009275	0.005479	-0.004328	-0.001562	-0.004032	0.000884	-0.014296	-0.002705	-0.000902	-0.000902	0.008958	-0.005206	0.017684
A1Cresult	-0.013318	0.016539	-0.147559	-0.021109	-0.043929	-0.020713	0.006512	0.058088	-0.006824	-0.009813	0.236383	-0.017477	0.013044	-0.024324	-0.004270	-0.049379	-0.091392	-0.044930	-0.031716	-0.032983	-0.043540	1.000000	0.051894	0.022541	-0.000669	-0.003225	0.022787	-0.001747	0.020844	0.009977	-0.005526	0.000223	0.009548	0.009374	0.007741	-0.003026	-0.000390	0.107227	-0.005008	0.001082	-0.001747	-0.001747	0.105614	0.086291	-0.013614
metformin	0.010548	0.001549	-0.060696	0.007304	0.008631	-0.008376	-0.033283	-0.009071	0.027596	0.023068	-0.044042	-0.038122	0.069433	-0.013006	-0.009572	-0.073780	0.033199	-0.018313	-0.024179	-0.073736	-0.029790	0.051894	1.000000	-0.001074	0.020372	-0.011841	0.047475	-0.002068	0.077111	0.129061	-0.006539	0.060566	0.097708	0.006246	0.005628	-0.003582	0.004664	-0.017392	-0.021191	-0.002748	0.008300	0.003116	0.325302	0.267566	-0.035809
repaglinide	0.025466	-0.004777	0.045565	-0.005440	-0.003481	-0.002759	-0.003732	0.034985	0.032986	0.010220	0.010438	0.005662	0.019283	0.001026	0.007820	0.011936	0.002242	0.003082	0.005636	0.033225	-0.015106	0.022541	-0.001074	1.000000	-0.003246	-0.003466	-0.007518	-0.000511	-0.015927	-0.024160	-0.001617	0.019393	0.009031	0.011257	0.018066	-0.000886	-0.002287	0.006058	-0.004506	-0.001534	-0.000511	-0.000511	0.071294	0.066174	0.014286
nateglinide	-0.004170	-0.005390	0.020363	0.010707	-0.008099	-0.008790	-0.019612	0.003320	0.014676	0.006590	-0.008292	-0.002359	0.023352	0.002719	0.005489	-0.006284	-0.000440	-0.000322	0.003922	0.012336	-0.016794	-0.000669	0.020372	-0.003246	1.000000	-0.002386	0.004488	-0.000352	-0.018191	-0.020817	-0.001113	0.025830	0.013947	-0.004585	0.018302	-0.000610	-0.001575	0.001396	-0.006775	-0.001056	-0.000352	-0.000352	0.052927	0.045552	0.007164
chlorpropamide	0.006801	0.006481	0.012367	-0.000839	0.007875	0.018525	0.002666	0.004094	-0.022046	0.002161	-0.005659	0.004757	-0.000940	-0.004402	-0.004218	-0.008317	-0.002017	-0.004128	-0.007445	-0.014080	0.008938	-0.003225	-0.011841	-0.003466	-0.002386	1.000000	-0.006537	-0.000121	-0.010745	-0.005823	-0.000383	-0.007959	-0.000123	-0.001576	-0.000581	-0.000210	-0.000541	-0.020008	-0.002329	-0.000363	-0.000121	-0.000121	-0.007035	0.015661	-0.002806
glimepiride	0.008261	-0.000156	0.044360	0.013694	-0.003178	-0.022360	-0.026685	0.016086	0.038055	0.012798	0.005344	0.007223	0.045223	-0.009039	0.003318	-0.016545	0.000410	0.006773	-0.010677	0.013640	-0.031840	0.022787	0.047475	-0.007518	0.004488	-0.006537	1.000000	-0.000964	-0.071983	-0.067334	-0.003050	0.042601	0.038655	0.018418	0.019830	0.009191	-0.004314	0.012479	-0.012202	-0.002894	-0.000964	-0.000964	0.138970	0.124797	0.004760
acetohexamide	0.001793	-0.003935	0.002400	-0.000736	-0.002988	0.014597	0.001296	0.013596	-0.004231	-0.002891	0.005344	0.006615	0.009348	-0.001242	-0.000907	-0.002118	0.001038	0.002264	0.000333	0.003449	-0.000902	-0.001747	-0.002068	-0.000511	-0.000352	-0.000121	-0.000964	1.000000	-0.001585	-0.001414	-0.000056	-0.001174	-0.001085	-0.000233	-0.000086	-0.000031	-0.000080	0.003607	-0.000344	-0.000054	-0.000018	-0.000018	0.004554	0.002311	0.002639
glipizide	0.018551	0.026810	0.055867	0.017062	0.007991	-0.013379	0.009300	0.016737	0.005875	0.007273	0.012450	0.004999	0.056985	0.010527	-0.003426	-0.022736	0.010541	0.004223	-0.005554	-0.005975	0.005931	0.020844	0.077111	-0.015927	-0.018191	-0.010745	-0.071983	-0.001585	1.000000	-0.104495	-0.005014	0.049752	0.041498	0.030598	0.002971	-0.002746	-0.001524	-0.027179	-0.027923	-0.000607	-0.001585	-0.001585	0.194260	0.205145	0.014766
glyburide	0.015784	0.034631	0.076798	0.008707	-0.002804	0.048256	0.004919	0.023482	-0.047599	-0.005929	-0.001768	0.001531	0.030886	-0.000482	-0.027870	-0.036659	0.017872	0.010435	-0.005157	-0.024247	0.000373	0.009977	0.129061	-0.024160	-0.020817	-0.005823	-0.067334	-0.001414	-0.104495	1.000000	-0.004473	0.027727	0.030766	0.015094	-0.000056	-0.002450	-0.006327	-0.071853	-0.006909	0.000245	-0.001414	-0.001414	0.172392	0.183024	-0.004492
tolbutamide	-0.001455	-0.001727	0.010110	-0.002326	0.006347	0.003228	0.001791	0.001799	-0.002662	-0.001944	-0.001320	-0.003423	0.002943	0.000350	-0.002870	-0.003545	0.006039	0.000339	-0.002879	0.001220	0.006437	-0.005526	-0.006539	-0.001617	-0.001113	-0.000383	-0.003050	-0.000056	-0.005014	-0.004473	1.000000	-0.003714	-0.003432	-0.000736	-0.000271	-0.000098	-0.000253	-0.001925	-0.001087	-0.000169	-0.000056	-0.000056	0.001000	0.007308	-0.007263
pioglitazone	0.026105	0.002339	0.013860	0.026059	0.018570	-0.014116	-0.005729	0.008521	0.034867	0.002210	-0.015599	0.016471	0.071584	0.012212	-0.001978	-0.026804	0.024890	0.000030	-0.008180	0.002278	-0.014531	0.000223	0.060566	0.019393	0.025830	-0.007959	0.042601	-0.001174	0.049752	0.027727	-0.003714	1.000000	-0.062763	0.015377	0.000791	-0.002034	-0.001659	0.003954	0.022117	0.007190	-0.001174	0.014894	0.203180	0.151949	0.011002
rosiglitazone	0.005938	0.010843	0.003034	0.004232	0.022930	-0.001694	-0.008894	0.008531	-0.008782	0.016639	-0.010260	0.018742	0.052860	-0.001550	-0.006844	-0.021471	0.010041	-0.010618	-0.003303	-0.011524	-0.009275	0.009548	0.097708	0.009031	0.013947	-0.000123	0.038655	-0.001085	0.041498	0.030766	-0.003432	-0.062763	1.000000	0.002006	0.003416	0.008079	-0.000996	0.004080	0.003340	-0.003256	-0.001085	-0.001085	0.191641	0.140410	0.005522
acarbose	0.013237	0.010581	0.008092	0.010411	0.006061	0.006779	-0.000753	0.007231	-0.002629	-0.005808	-0.000654	-0.000362	0.017947	0.009388	0.004224	0.000411	0.003061	0.000704	0.000458	0.007741	0.005479	0.009374	0.006246	0.011257	-0.004585	-0.001576	0.018418	-0.000233	0.030598	0.015094	-0.000736	0.015377	0.002006	1.000000	-0.001117	-0.000403	-0.001040	-0.001790	0.013046	-0.000698	-0.000233	-0.000233	0.047261	0.030097	0.007816
miglitol	-0.001307	0.009920	0.011788	-0.003532	-0.001414	0.005779	-0.000763	0.005083	0.011455	0.002816	-0.002963	-0.001521	0.006422	-0.002243	-0.000207	-0.003851	0.006030	0.005705	-0.000319	-0.000293	-0.004328	0.007741	0.005628	0.018066	0.018302	-0.000581	0.019830	-0.000086	0.002971	-0.000056	-0.000271	0.000791	0.003416	-0.001117	1.000000	-0.000148	-0.000383	0.000451	-0.001650	-0.000257	-0.000086	-0.000086	0.018472	0.011094	0.003413
troglitazone	0.003106	0.007860	-0.001978	-0.001274	0.003307	0.008684	0.002245	0.004746	-0.007329	-0.002660	0.005036	-0.005745	0.002992	-0.002152	-0.001572	-0.003669	0.000456	0.001300	-0.000620	0.004710	-0.001562	-0.003026	-0.003582	-0.000886	-0.000610	-0.000210	0.009191	-0.000031	-0.002746	-0.002450	-0.000098	-0.002034	0.008079	-0.000403	-0.000148	1.000000	-0.000138	-0.000391	-0.000595	-0.000093	-0.000031	-0.000031	0.007888	0.004002	0.001009
tolazamide	0.003990	0.003242	0.003605	0.000692	0.010291	0.013139	0.001834	0.000328	-0.015677	-0.004689	0.000008	0.005154	-0.002113	-0.005556	-0.004059	-0.003526	0.000745	-0.003710	-0.003145	-0.013444	-0.004032	-0.000390	0.004664	-0.002287	-0.001575	-0.000541	-0.004314	-0.000080	-0.001524	-0.006327	-0.000253	-0.001659	-0.000996	-0.001040	-0.000383	-0.000138	1.000000	-0.013867	-0.001537	-0.000240	-0.000080	-0.000080	-0.002376	0.010336	-0.007513
insulin	-0.039862	0.000247	-0.079078	-0.076697	-0.025368	-0.041842	0.005094	0.101223	0.115265	-0.014342	0.085401	0.015020	0.198963	0.010029	0.048501	0.060505	-0.075260	-0.007776	0.013942	0.076730	0.000884	0.107227	-0.017392	0.006058	0.001396	-0.020008	0.012479	0.003607	-0.027179	-0.071853	-0.001925	0.003954	0.004080	-0.001790	0.000451	-0.000391	-0.013867	1.000000	0.005828	-0.000677	0.003607	0.003607	0.461502	0.525169	0.040750
glyburide.metformin	0.006384	0.002489	-0.002451	-0.014159	-0.000573	-0.002994	-0.024616	-0.006358	0.055730	0.000051	-0.010852	-0.000553	0.013382	-0.008428	0.001956	-0.008426	0.015281	-0.007621	-0.000101	-0.005894	-0.014296	-0.005008	-0.021191	-0.004506	-0.006775	-0.002329	-0.012202	-0.000344	-0.027923	-0.006909	-0.001087	0.022117	0.003340	0.013046	-0.001650	-0.000595	-0.001537	0.005828	1.000000	0.050992	-0.000344	-0.000344	0.038712	0.044474	-0.001842
glipizide.metformin	0.005380	0.007965	0.003658	-0.002207	-0.005046	0.000933	-0.000281	-0.001692	0.010871	-0.006234	-0.006685	-0.006640	0.002757	0.003037	-0.002723	-0.000813	0.004664	0.005659	0.006007	-0.006428	-0.002705	0.001082	-0.002748	-0.001534	-0.001056	-0.000363	-0.002894	-0.000054	-0.000607	0.000245	-0.000169	0.007190	-0.003256	-0.000698	-0.000257	-0.000093	-0.000240	-0.000677	0.050992	1.000000	-0.000054	-0.000054	0.010838	0.006933	0.001747

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide.metformin	glipizide.metformin	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed
readmitted
0	2.589060	0.472088	6.050665	0.098717	2.025994	3.818606	5.613665	4.254415	4.325617	11.423771	42.454957	1.404061	15.639725	0.272042	0.105496	0.382036	14.233018	11.918612	11.127522	7.220914	0.085191	0.376315	0.422912	0.026324	0.012666	0.001654	0.099345	0.000000	0.243336	0.211820	0.000529	0.137245	0.119783	0.004928	0.000628	0.000066	0.000926	1.017792	0.013030	0.000265	0.000066	0.000066	0.438587	0.744857
1	2.614930	0.454177	6.146121	0.164091	2.012887	3.324383	5.958053	4.508703	4.467834	9.700056	43.880269	1.250655	16.344458	0.495456	0.294039	0.845356	14.223700	12.124352	11.623351	7.658366	0.098475	0.367613	0.381732	0.034091	0.016122	0.002310	0.108230	0.000103	0.270370	0.210197	0.000205	0.162448	0.139241	0.008472	0.001284	0.000205	0.000616	1.097808	0.014376	0.000411	0.000000	0.000000	0.491605	0.799096
2	2.624821	0.459666	6.164041	0.120923	1.985521	4.487828	5.820366	4.747176	4.274781	10.037232	44.156563	1.272076	16.748449	0.428640	0.335402	1.218457	14.086396	12.104694	11.639300	7.672554	0.105489	0.332538	0.336356	0.034208	0.014638	0.000318	0.100239	0.000000	0.261098	0.201273	0.000000	0.138584	0.113286	0.005410	0.000636	0.000000	0.000000	1.135561	0.010501	0.000318	0.000000	0.000000	0.488942	0.799204

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
0	3	0	8	0	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
1	3	0	7	0	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	0	2	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	1	1	0

	Unnamed: 0	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	metformin	glyburide	insulin	change	diabetesMed	readmitted
0	0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	10	4	18	7	0	2	0	0	1	1
1	1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	16	13	16	9	2	2	3	1	1	0
2	2	5	0	6	1	22	7	4	14	18	29	2	18	0	1	24	18	2	9	0	0	2	0	1	0
3	3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	17	4	3	9	0	0	2	0	1	1
4	4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	23	18	32	9	0	0	0	0	0	0

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
0	3	0	8	0	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
1	3	0	7	0	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	0	2	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	1	1	0

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
count	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000	56000.0	56000.0	56000.000000	56000.000000	56000.000000	56000.0	56000.000000	56000.000000	56000.000000	56000.000000	56000.000000
mean	2.602071	0.464464	6.096589	0.123946	2.016893	3.721821	5.756643	4.398161	4.369375	10.668643	43.141661	1.335893	16.009268	0.367321	0.196875	0.637054	14.213321	12.011054	11.357411	7.423750	0.092089	0.368375	0.398875	0.029911	0.014089	0.001732	0.102536	0.000036	0.254732	0.210071	0.000357	0.146161	0.125821	0.006214	0.000857	0.000107	0.000714	0.0	0.0	1.058839	0.013214	0.000321	0.0	0.000036	0.000036	0.462679	0.769821	0.572268
std	0.937754	0.498740	1.590761	0.712004	1.438340	5.291517	4.053838	2.984346	4.363828	15.595799	19.656507	1.702009	8.132455	1.249570	0.916820	1.270768	7.272908	7.443902	8.157131	1.931488	0.431655	0.890972	0.815169	0.247161	0.169132	0.060480	0.449274	0.008452	0.678992	0.627625	0.026724	0.525985	0.490002	0.112904	0.042249	0.014638	0.037790	0.0	0.0	1.102484	0.162472	0.025353	0.0	0.008452	0.008452	0.498610	0.420951	0.685018
min	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000
25%	3.000000	0.000000	5.000000	0.000000	1.000000	1.000000	1.000000	2.000000	0.000000	0.000000	32.000000	0.000000	10.000000	0.000000	0.000000	0.000000	10.000000	4.000000	3.000000	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	1.000000	0.000000
50%	3.000000	0.000000	6.000000	0.000000	1.000000	1.000000	7.000000	4.000000	6.000000	4.000000	44.000000	1.000000	15.000000	0.000000	0.000000	0.000000	15.000000	12.000000	10.000000	8.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	1.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	1.000000	0.000000
75%	3.000000	1.000000	7.000000	0.000000	3.000000	4.000000	7.000000	6.000000	7.000000	18.000000	57.000000	2.000000	20.000000	0.000000	0.000000	1.000000	18.000000	17.000000	17.000000	9.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	2.000000	0.000000	0.000000	0.0	0.000000	0.000000	1.000000	1.000000	1.000000
max	5.000000	1.000000	9.000000	9.000000	8.000000	28.000000	25.000000	14.000000	16.000000	63.000000	132.000000	6.000000	75.000000	42.000000	76.000000	21.000000	32.000000	32.000000	32.000000	16.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	2.000000	3.000000	3.000000	2.000000	3.000000	3.000000	3.000000	3.000000	2.000000	2.000000	0.0	0.0	3.000000	3.000000	2.000000	0.0	2.000000	2.000000	1.000000	1.000000	2.000000

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	metformin	glyburide	insulin	change	diabetesMed	readmitted
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	10	4	18	7	0	2	0	0	1	1
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	16	13	16	9	2	2	3	1	1	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	1	24	18	2	9	0	0	2	0	1	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	17	4	3	9	0	0	2	0	1	1
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	23	18	32	9	0	0	0	0	0	0

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readmitted	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	1	1
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	1	1
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0	0
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	1	1
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0	0
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0	0
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0	0
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	2	2

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readmitted	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	1	>30
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0	NO
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0	NO
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	1	>30
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0	NO
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0	NO
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0	NO
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	1	>30
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0	NO
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0	NO
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0	NO
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	2	<30

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readmitted	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	1	0
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	1	0
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0	0
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	1	0
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0	0
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0	0
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0	0
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	2	1

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	0
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	0
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	0
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	1

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide.metformin	glipizide.metformin	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed
readm2
0	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715	49715
1	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285	6285

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
0	3	0	8	0	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
1	3	0	7	0	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	0	2	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	1	1	0

	Unnamed: 0	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	metformin	glyburide	insulin	change	diabetesMed	readmitted
0	0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	10	4	18	7	0	2	0	0	1	1
1	1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	16	13	16	9	2	2	3	1	1	0
2	2	5	0	6	1	22	7	4	14	18	29	2	18	0	1	24	18	2	9	0	0	2	0	1	0
3	3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	17	4	3	9	0	0	2	0	1	1
4	4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	23	18	32	9	0	0	0	0	0	0

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide.metformin	glipizide.metformin	glimepiride.pioglitazone	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed	readmitted
0	3	0	8	0	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
1	3	0	7	0	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	0	2	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	1	1	0

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	metformin	glyburide	insulin	change	diabetesMed	readmitted
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	10	4	18	7	0	2	0	0	1	1
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	16	13	16	9	2	2	3	1	1	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	1	24	18	2	9	0	0	2	0	1	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	17	4	3	9	0	0	2	0	1	1
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	23	18	32	9	0	0	0	0	0	0

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readmitted	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	1	1
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	1	1
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0	0
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	1	1
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0	0
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0	0
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0	0
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	2	2

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readmitted	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	1	>30
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0	NO
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0	NO
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	1	>30
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0	NO
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0	NO
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0	NO
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	1	>30
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0	NO
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0	NO
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0	NO
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	2	<30

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readmitted	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	1	0
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	1	0
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0	0
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	1	0
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0	0
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0	0
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0	0
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	2	1

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed	readm2
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1	0
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1	0
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1	0
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1	0
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1	0
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1	0
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1	0
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1	0
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1	0
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1	1

	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide.metformin	glipizide.metformin	metformin.rosiglitazone	metformin.pioglitazone	change	diabetesMed
readm2
0	2.599195	0.465071	6.088062	0.124329	2.020859	3.624982	5.748587	4.354038	4.381334	10.748466	43.013356	1.343961	15.915820	0.35957	0.179362	0.563552	14.229367	11.999216	11.321774	7.392296	0.090395	0.372906	0.406779	0.029367	0.014020	0.001911	0.102826	0.00004	0.253927	0.211184	0.000402	0.147119	0.127406	0.006316	0.000885	0.000121	0.000805	1.049140	0.013557	0.000322	0.00004	0.00004	0.459358	0.766107
1	2.624821	0.459666	6.164041	0.120923	1.985521	4.487828	5.820366	4.747176	4.274781	10.037232	44.156563	1.272076	16.748449	0.42864	0.335402	1.218457	14.086396	12.104694	11.639300	7.672554	0.105489	0.332538	0.336356	0.034208	0.014638	0.000318	0.100239	0.00000	0.261098	0.201273	0.000000	0.138584	0.113286	0.005410	0.000636	0.000000	0.000000	1.135561	0.010501	0.000318	0.00000	0.00000	0.488942	0.799204

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	MED_SPEC_NUM	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	DIAG_CAT_1	DIAG_CAT_2	DIAG_CAT_3	number_diagnoses	A1Cresult	metformin	glipizide	glyburide	pioglitazone	insulin	change	diabetesMed
0	3	0	8	3	1	4	5	0	0	39	3	11	0	0	0	10	4	18	7	0	0	0	2	0	0	0	1
1	3	0	7	5	3	1	6	7	0	79	1	25	3	0	0	16	13	16	9	0	2	0	2	0	3	1	1
2	5	0	6	1	22	7	4	14	18	29	2	18	0	0	1	24	18	2	9	0	0	0	0	0	2	0	1
3	3	1	7	1	1	7	3	0	18	72	3	18	0	0	0	17	4	3	9	0	0	0	0	0	2	0	1
4	3	0	3	2	1	1	3	0	0	21	1	6	0	0	0	23	18	32	9	0	0	0	0	0	0	0	0
5	3	1	7	2	1	1	2	0	18	4	0	7	0	0	0	14	9	3	8	0	0	0	0	0	0	0	0
6	3	0	4	1	1	7	6	14	33	89	0	25	0	2	1	25	10	16	9	0	2	0	0	0	2	1	1
7	3	0	7	1	6	7	4	6	0	63	0	22	0	2	4	16	3	3	5	0	2	0	0	0	0	0	1
8	3	1	6	1	1	7	6	7	0	45	0	24	0	0	0	16	9	3	7	3	2	0	0	2	1	1	1
9	3	1	8	1	1	7	2	3	0	45	0	13	0	0	0	17	3	3	9	0	0	2	0	0	1	1	1
10	3	1	6	2	1	1	3	7	0	57	6	21	0	0	0	12	10	4	9	0	0	0	0	0	2	0	1
11	3	0	4	6	1	17	6	0	18	81	0	26	0	0	0	13	21	21	9	3	0	2	0	2	0	1	1